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  • 1.
    Abdel-Rahman, Suzan
    et al.
    Department of Demography and Biostatistics, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt.
    Awwad, Fuad A.
    Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh, 11587, Saudi Arabia.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Abonazel, Mohamed R.
    Department of Applied Statistics and Econometrics, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt.
    New evidence of gender inequality during COVID-19 outbreak in the Middle East and North Africa2023In: Heliyon, E-ISSN 2405-8440, Vol. 9, no 7, article id e17705Article in journal (Refereed)
    Abstract [en]

    The COVID-19 pandemic has significantly altered employment and income distribution, impacting women and men differently. This study investigates the negative effects of COVID-19 on the labour market, focusing on the gender gap in five countries in the Middle East and North Africa (MENA) region. The study indicates whether women are more susceptible to losing their jobs, either temporarily or permanently, switching their primary occupation, and experiencing decreased working hours and income compared to men during the COVID-19 outbreak. The study utilizes a multivariate Probit model to estimate the relationship between gender and adverse labour outcomes controlling for correlations among outcomes. Data are obtained from the Combined COVID-19 MENA Monitor Household Survey, covering Egypt, Tunisia, Morocco, Jordan, and Sudan. The findings of this study offer empirical evidence of the gender gap in labour market outcomes during the pandemic. Women are more likely than men to experience negative work outcomes, such as permanent job loss and change in their main job. The increased childcare and housework responsibilities have significantly impacted women's labour market outcomes during the pandemic. However, the availability of telework has reduced the likelihood of job loss among women. The study's results contribute to a better understanding of the impact of COVID-19 on gender inequality in understudied MENA countries. Mitigation policies should focus on supporting vulnerable women who have experienced disproportionate negative effects of COVID-19.

  • 2.
    Akram, Muhammad N.
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    A new biased estimator for the gamma regression model: Some applications in medical sciences2023In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 52, no 11, p. 3612-3632Article in journal (Refereed)
    Abstract [en]

    The Gamma Regression Model (GRM) has a variety of applications in medical sciences and other disciplines. The results of the GRM may be misleading in the presence of multicollinearity. In this article, a new biased estimator called James-Stein estimator is proposed to reduce the impact of correlated regressors for the GRM. The mean squared error (MSE) properties of the proposed estimator are derived and compared with the existing estimators. We conducted a simulation study and employed the MSE and bias evaluation criterion to judge the proposed estimator’s performance. Finally, two medical dataset are considered to show the benefit of the proposed estimator over existing estimators.

  • 3.
    Akram, Muhammad Nauman
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A new Liu-type estimator for the Inverse Gaussian Regression Model2020In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 90, no 7, p. 1153-1172Article in journal (Refereed)
    Abstract [en]

    The Inverse Gaussian Regression Model (IGRM) is used when the response variable is positively skewed and follows the inverse Gaussian distribution. In this article, we propose a Liu-type estimator to combat multicollinearity in the IGRM. The variance of the Maximum Likelihood Estimator (MLE) is overstated due to the presence of severe multicollinearity. Moreover, some estimation methods are suggested to estimate the optimal value of the shrinkage parameter. The performance of the proposed estimator is compared with the MLE and some other existing estimators in the sense of mean squared error through Monte Carlo simulation and different real-life applications. Under certain conditions, it is concluded that the proposed estimator is superior to the MLE, ridge, and Liu estimator.

  • 4.
    Alheety, Mustafa I.
    et al.
    Univ Anbar, Dept Math, Coll Educ Pure Sci, Ramadi, Iraq..
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. Golam
    Florida Int Univ, Dept Math & Stat, Miami, FL 33199 USA..
    Modifed almost unbiased two-parameter estimator for the Poisson regression model with an application to accident data2021In: SORT - Statistics and Operations Research Transactions, ISSN 1696-2281, E-ISSN 2013-8830, Vol. 45, no 2, p. 121-142Article in journal (Refereed)
    Abstract [en]

    Due to the large amount of accidents negatively affecting the wellbeing of the survivors and their families, a substantial amount of research is conducted to determine the causes of road accidents. This type of data come in the form of non-negative integers and may be modelled using the Poisson regression model. Unfortunately, the commonly used maximum likelihood estimator is unstable when the explanatory variables of the Poisson regression model are highly correlated. Therefore, this paper proposes a new almost unbiased estimator which reduces the instability of the maximum likelihood estimator and at the same time produce smaller mean squared error. We study the sta-tistical properties of the proposed estimator and a simulation study has been conducted to compare the performance of the estimators in the smaller mean squared error sense. Finally, Swedish traffic fatality data are analyzed to show the beneft of the proposed method.

  • 5.
    Alka,
    et al.
    Department of Mathematics and Statistics, Banasthali University, Jaipur, Rajasthan, India.
    Rai, Piyush Kant
    Department of Statistics, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
    Qasim, Muhammad
    Department of Statistics & Computer Science, University of Veterinary & Animal Sciences, Lahore, Pakistan.
    Two-step calibration of design weights under two auxiliary variables in sample survey2019In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 12, p. 2316-2327Article in journal (Refereed)
    Abstract [en]

    Calibration on the available auxiliary variables is widely used to increase the precision of the estimates of parameters. Singh and Sedory [Two-step calibration of design weights in survey sampling. Commun Stat Theory Methods. 2016;45(12):3510–3523.] considered the problem of calibration of design weights under two-step for single auxiliary variable. For a given sample, design weights and calibrated weights are set proportional to each other, in the first step. While, in the second step, the value of proportionality constant is determined on the basis of objectives of individual investigator/user for, for example, to get minimum mean squared error or reduction of bias. In this paper, we have suggested to use two auxiliary variables for two-step calibration of the design weights and compared the results with single auxiliary variable for different sample sizes based on simulated and real-life data set. The simulated and real-life application results show that two-auxiliary variables based two-step calibration estimator outperforms the estimator under single auxiliary variable in terms of minimum mean squared error. 

  • 6.
    Alka, Singh
    et al.
    Banasthali Univ, Dept Math & Stat, Jaipur 304022, Rajasthan, India..
    Rai, Piyush Kant
    Banaras Hindu Univ, Dept Stat, Varanasi 221005, Uttar Pradesh, India..
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Two-Step Calibration Estimator with Double Use of Auxiliary Variable: Method and Application2022In: Journal of the Iranian Statistical Society, ISSN 1726-4057, Vol. 21, no 1, p. 37-54Article in journal (Refereed)
    Abstract [en]

    This article introduces a two-step calibration technique for the inverse relationship between study variable and auxiliary variable along with the double use of the auxiliary variable. In the first step, the calibration weights and design weights are set proportional to each other for a given sample. While in the second step, the constant of proportionality is to be obtained on the basis of some different objectives of the investigation viz. bias reduction or minimum Mean Squared Error (MSE) of the proposed estimator. Many estimators based on inverse relationship between x and y have been already developed and are considered to be special cases of the proposed estimator. Properties of the proposed estimator is discussed in details. Moreover, a simulation study has also been conducted to compare the performance of the proposed estimator under Simple Random Sampling Without Replacement (SRSWOR) and Lahiri-Midzuno (L-M) sampling design in terms of percent relative bias and MSE. The benefits of two-step calibration estimator are also demonstrated using real life data.

  • 7.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Aslam, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Influence diagnostics in gamma ridge regression model2019In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 3, p. 536-556Article in journal (Refereed)
    Abstract [en]

    In this article, we proposed some influence diagnostics for the gamma regression model (GRM) and the gamma ridge regression model (GRRM). We assess the impact of influential observations on the GRM and GRRM estimates by extending the work of Pregibon [Logistic regression diagnostics. Ann Stat. 1981;9:705–724] and Walker and Birch [Influence measures in ridge regression. Technometrics. 1988;30:221–227]. Comparison of both models is made and demonstrated with the help of a simulation study and a real data set. We report some momentous results in detecting the influential observations and their effects on the GRM and GRRM estimates. 

  • 8.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Punjab, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Afzal, Saima
    Department of Statistics, Bahauddin Zakariya University, Multan, Punjab, Pakistan.
    Naveed, Khalid
    Department of Statistics, Bahauddin Zakariya University, Multan, Punjab, Pakistan.
    New ridge estimators in the inverse Gaussian regression: Monte Carlo simulation and application to chemical data2022In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 51, no 10, p. 6170-6187Article in journal (Refereed)
    Abstract [en]

    In numerous application areas, when the response variable is continuous, positively skewed, and well fitted to the inverse Gaussian distribution, the inverse Gaussian regression model (IGRM) is an effective approach in such scenarios. The problem of multicollinearity is very common in several application areas like chemometrics, biology, finance, and so forth. The effects of multicollinearity can be reduced using the ridge estimator. This research proposes new ridge estimators to address the issue of multicollinearity in the IGRM. The performance of the new estimators is compared with the maximum likelihood estimator and some other existing estimators. The mean square error is used as a performance evaluation criterion. A Monte Carlo simulation study is conducted to assess the performance of the new ridge estimators based on the minimum mean square error criterion. The Monte Carlo simulation results show that the performance of the proposed estimators is better than the available methods. The comparison of proposed ridge estimators is also evaluated using two real chemometrics applications. The results of Monte Carlo simulation and real applications confirmed the superiority of the proposed ridge estimators to other competitor methods.

  • 9.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Performance of Asar and Genç and Huang and Yang’s Two-Parameter Estimation Methods for the Gamma Regression Model2019In: Iranian Journal of Science and Technology, Transactions A: Science, ISSN 1028-6276, Vol. 43, no 6, p. 2951-2963Article in journal (Refereed)
    Abstract [en]

    This study assesses the performance of two-parameter estimation methods to combat multicollinearity in the Gamma regression model. We derived optimal values for two-parameter estimation methods in the Gamma regression model. Furthermore, we proposed some estimation methods to estimate the shrinkage parameters and these methods improve the efficiency of the two-parameter estimator. We compare the performance of these estimators by means of Monte Carlo simulation study where the mean squared error (MSE) is considered as a performance criterion. Finally, consider a reaction rate data to evaluate the performance of the estimators. The simulation and numerical example results showed that the two-parameter biased estimators have smaller MSE than the maximum likelihood estimator under certain conditions.

  • 10.
    Amin, Muhammad
    et al.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Afzal, Saima
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Performance of some ridge estimators for the gamma regression model2020In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798, Vol. 61, no 3, p. 997-1026Article in journal (Refereed)
    Abstract [en]

    In this study, we proposed some ridge estimators by considering the work of Månsson (Econ Model 29(2):178–184, 2012), Dorugade (J Assoc Arab Univ Basic Appl Sci 15:94–99, 2014) and some others for the gamma regression model (GRM). The GRM is a special form of the generalized linear model (GLM), where the response variable is positively skewed and well fitted to the gamma distribution. The commonly used method for estimation of the GRM coefficients is the maximum likelihood (ML) estimation method. The ML estimation method perform better, if the explanatory variables are uncorrelated. There are the situations, where the explanatory variables are correlated, then the ML estimation method is incapable to estimate the GRM coefficients. In this situation, some biased estimation methods are proposed and the most popular one is the ridge estimation method. The ridge estimators for the GRM are proposed and compared on the basis of mean squared error (MSE). Moreover, the outperforms of proposed ridge estimators are also calculated. The comparison has done using a Monte Carlo simulation study and two real data sets. Results show that Kibria’s and Månsson and Shukur’s methods are preferred over the ML method. 

  • 11.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Yasin, Ahad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Almost unbiased ridge estimator in the gamma regression model2022In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 51, no 7, p. 3830-3850Article in journal (Refereed)
    Abstract [en]

    This article introduces the almost unbiased gamma ridge regression estimator (AUGRRE) estimator based on the gamma ridge regression estimator (GRRE). Furthermore, some shrinkage parameters are proposed for the AUGRRE. The performance of the AUGRRE by using different shrinkage parameters is compared with the existing GRRE and maximum likelihood estimator. A Monte Carlo simulation is carried out to assess the performance of the estimators where the bias and mean squared error performance criteria are used. We also used a real-life dataset to demonstrate the benefit of the proposed estimators. The simulation and real-life example results show the superiority of AUGRRE over the GRRE and the maximum likelihood estimator for the gamma regression model with collinear explanatory variables.

  • 12.
    Amin, Muhammad
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Ullah, Muhammad Aman
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Diagnostic techniques for the inverse Gaussian regression model2022In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 51, no 8, p. 2552-2564Article in journal (Refereed)
    Abstract [en]

    In this article, we propose some diagnostic techniques for the inverse Gaussian regression model (IGRM), which are appropriate for modeling the response variable that undertakes positively skewed continuous dataset. Moreover, two new diagnostic methods are mainly proposed for the IGRM, which named as covariance ratio (CVR) and Welsch?s distance (WD). The comparison of our proposed methods of influence diagnostics with the existing approaches has been made through Monte Carlo simulation under different factors. In addition, the benefit of the proposed methods is assessed using a real application. Based on the simulation and empirical application results, we observed that the performance of the proposed method is better than the existing methods for detection of influential observations.

  • 13.
    Amjad, M.
    et al.
    University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Saleem, M. H.
    University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Iqbal, M. Z.
    University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Hassan, A.
    Department of Veterinary Surgery and Pet Sciences, Faculty of Veterinary Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Jabbar, A.
    University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Ashraf, M.
    University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Ullah, A.
    University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Tolba, M. M.
    Biomedical Informatics and Biotechnology Group, Informatics and Systems Department, Division of Engineering Research, National Research Centre, Cairo, Egypt.
    Nasser, H. A.
    Microbiology and Public Health Department. Faculty of Pharmacy, Helipolis, University, Egypt.
    Naaz, S.
    Livestock and Dairy Development Department Punjab Pakistan.
    Ahmad, I.
    Department of Veterinary Clinical Sciences, University of Poonch Rawalakot Azad Kashmir, Pakistan.
    Efficacy of Quinapyramine Sulphate, Isometamedium Chloride and Diminazene Aceturate For Treatment of Surra2022In: Journal of Animal and Plant Sciences, ISSN 1018-7081, Vol. 32, no 3, p. 663-669Article in journal (Refereed)
    Abstract [en]

    Trypanosomiasis (Surra) is a parasitic and zoonotic disease caused by Trypanosoama evansi, transmitted by insect vectors Tabanus and Stomoxys mechanically. The aim of the present study was to determine the therapeutic efficacy of various trypanosidal drugs against trypanosomiasis in Thoroughbred horses. Horses having clinical signs of trypanosomiasis were diagnosed through blood smear through a microscope were selected for this study. The infected horses were divided into three experimental groups for therapeutic trials. Animals in group A were treated with a single dose of quinapyramine sulphate @ 3000mg/ml per 50/kg body weight; group B was treated with a single dose of isometamedium chloride Hydrochloride@ 0.5 mg/2.5 ml of 1% solution per 50/kg body weight; group C was treated with a single dose of diminazene aceturate@ 2360 mg/15 ml per 100/kg. Results revealed that significant (P<0.0001) de cline in the values of erythrocyte counts (RBC), hemoglobin concentration (Hb), packed cell volume (PCV), platelets (PLT) and a significant (P<0.0001) increase in white blood cells (WBC), granulocytes, and monocytes in infected horses as compared to healthy ones. Therapeutic trials indicated that quinapyramine sulphate that showed 100% efficacy at 21th days had significantly higher than isometamedium chloride and diminazene aceturate (95.83 and 75% efficacy, respectively). The hematological parameters of recovered horses were significantly restored to normal values on day 21 after treatment. It is concluded that quinapyramine sulphate is the drug of choice against trypanosomiasis in Thoroughbred horses.

  • 14.
    Asif, M.
    et al.
    Department of Statistics, Govt. Degree College, Qadir Pur Raan, Multan, Pakistan.
    Aslam, M.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Khan, S.
    Department of Preventive Pediatrics, Children Hospital and Institute of Child Health, Multan, Pakistan.
    Altaf, S.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Ahmad, S.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Ali, H.
    Department of Zoology, Govt. Degree College, Qadir Pur Raan, Multan, Pakistan.
    Wyszyńska, J.
    Medical College of Rzeszów University, University of Rzeszów, ul. Kopisto 2a, Rzeszów, 35-959, Poland.
    Developing Neck Circumference Growth Reference Charts for Pakistani Children and Adolescents Using the Lambda-Mu-Sigma (LMS) and Quantile Regression Method2021In: Public Health Nutrition, ISSN 1368-9800, E-ISSN 1475-2727, Vol. 24, no 17, p. 5641-5649Article in journal (Refereed)
    Abstract [en]

    Objective: Neck circumference (NC) is currently used as an embryonic marker of obesity and its associated risks. But its use in clinical evaluations and other epidemiological purposes requires sex and age-specific standardized cut-offs which are still scarce for the Pakistani pediatric population. We therefore developed sex and age-specific growth reference charts for NC for Pakistani children and adolescents aged 2-18 years.

    Design: Cross-sectional multi-ethnic anthropometric survey (MEAS) study.

    Setting: Multan, Lahore, Rawalpindi and Islamabad.

    Participants: The dataset of 10,668 healthy Pakistani children and adolescents aged 2 to 18 years collected in MEAS were used. Information related to age, sex and NC were taken as study variables. The lambda-mu-sigma (LMS) and quantile regression (QR) methods were applied to develop growth reference charts for NC.

    Results: The 5th, 10th, 25th, 50th, 75th, 90th and 95th smoothed percentile values of NC were presented. The centile values showed that neck size increased with age in both boys and girls. During 8 and 14 years of age, girls were found to have larger NC than boys. A comparison of NC median (50th) percentile values with references from Iranian and Turkish populations reveals substantially lower NC percentiles in Pakistani children and adolescents compared to their peers in the reference population.

    Conclusion: The comparative results suggest that the uses of NC references of developed countries are inadequate for Pakistani children. A small variability between empirical centiles and centiles obtained by QR procedure recommends that growth charts should be constructed by QR as an alternative method.

  • 15.
    Asif, M.
    et al.
    Department of Statistics, Govt. Degree College Qadir Pur Raan, Multan, Pakistan.
    Aslam, M.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Altaf, S.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Ismail, A.
    Institute of Food Science and Nutrition, Faculty of Agricultural Sciences, Bahauddin Zakariya University, Multan, Pakistan.
    Ali, H.
    Department of Zoology, Govt. Degree College Qadir Pur Raan, Multan, Pakistan.
    A dataset about anthropometric measurements of the Pakistani children and adolescents using a cross-sectional multi-ethnic anthropometric survey2021In: Data in Brief, E-ISSN 2352-3409, Vol. 34, article id 106642Article in journal (Refereed)
    Abstract [en]

    Evaluation of nutritional status is necessary during childhood and the juvenile years when the level of hydration and the adipose tissues experience significant changes. Anthropometric measurements and their derived indices are valid proxies to predict body fat, obesity (general or central) and their associated cardiovascular risks. The dataset under consideration also provides the socio-demographic related information and anthropometric measurement values related to height, weight, body mass index (BMI), waist circumference (WC), hip circumference (HpC), waist-to-hip ratio (WHpR), waist-to-height ratio (WHtR), mid-upper arm circumference (MUAC), neck circumference (NC), and wrist circumference (WrC). Standard procedure was adopted for quantifying the body measurements. The data were consisting of 10,782 children and adolescents aged 2–19 years, belonging four major cities of Pakistan viz. Multan, Lahore, Rawalpindi and Islamabad. This dataset is beneficial to develop anthropometric growth charts which will provide the essential knowledge of growth and nutritional disorders (e.g., stunted, overweight and obesity) of Pakistani children and adolescents. The dataset can also be used by researchers to calculate body surface area (BSA), body frame size (BFS), body shape index (BSI), and tri-ponderal mass index (TMI) of children and adolescents that are also some other reliable indicators of obesity and insulin resistance as well as cardiometabolic risk in children and adults.

  • 16.
    Asif, Muhammad
    et al.
    Govt Degree Coll, Dept Stat, Multan, Punjab, Pakistan..
    Aslam, Muhammad
    Bahauddin Zakariya Univ, Dept Stat, Multan, Pakistan..
    Ullah, Kalim
    Kohat Univ Sci & Technol KUST, Dept Zool, Kohat, Pakistan..
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Afzal, Khurram
    Bahauddin Zakariya Univ, Inst Food Sci 6 Nutr, Multan, Pakistan..
    Abbas, Asad
    Bahauddin Zakariya Univ, Inst Food Sci 6 Nutr, Multan, Pakistan..
    Ali, Manzar
    Ibne Siena Hosp & Res Inst, Multan Med & Dent Coll, Multan, Pakistan..
    Younis, Muhammad
    Bahauddin Zakariya Univ, Inst Food Sci 6 Nutr, Multan, Pakistan..
    Ullah, Sami
    Univ Peshawar, Dept Pharm, Peshawar, Kpk, Pakistan..
    Bin Asad, Muhammad Hassham Hassan
    COMSATS Univ, Dept Pharm, Abbotabad Campus, Islamabad 22060, Pakistan.;Kazan Fed Univ, Inst Fundamental Med, Dept Genet, Kazan, Russia..
    Wyszynska, Justyna
    Rzeszow Univ, Med Coll, Ul Kopisto 2a, PL-35959 Rzeszow, Poland..
    Diagnostic Performance and Appropriate Cut-Offs of Different Anthropometric Indicators for Detecting Children with Overweight and Obesity2021In: BioMed Research International, ISSN 2314-6133, E-ISSN 2314-6141, article id 1608760Article in journal (Refereed)
    Abstract [en]

    In the clinical settings, different anthropometric indicators like neck circumference (NC), waist circumference (WC), midupper arm circumference (MUAC), waist-to-height ratio (WHtR), and arm-to-height ratio (AHtR) have been suggested for evaluating overweight and obesity in children. The comparative ability of these indicators in Pakistan is yet unknown. This study is aimed at examining the validity of different anthropometric indicators of overweight and obesity simultaneously and at determining their superlative cut-off values that would correctly detect overweight and obesity in children. For this purpose, the dataset of anthropometric measurements height, weight, WC, MUAC, and NC of 5,964 Pakistani children, aged 5-12 years collected in a cross-sectional multiethnic anthropometric survey (MEAS), was used. Receiver operating characteristic (ROC) curve analysis was performed to assess the validity of different anthropometric indicators. The most sensitive and specific cut-off points, positive and negative predictive values of each indicator were also calculated. The results of the ROC curve indicated that all the studied indicators had a good performance but the indicators AHtR and WHtR had the highest value of the area under the curve (AUC) for the screening of children with overweight and obesity (AUC > 0.80). In the overall sample, AHtR, WHtR, MUAC, WC, and NC cut-off points indicative of overweight, in both boys and girls, were 0.14, 0.46, 18.41 cm, 62.86 cm, and 26.36 cm and 0.14, 0.47, 18.16 cm, 64.39 cm, and 26.54 cm, respectively; the corresponding values for obesity were 0.14, 0.47, 18.67 cm, 62.10 cm, and 26.36 cm and 0.14, 0.48, 20.19 cm, 64.39 cm, and 25.27 cm. We concluded that the sex-specific cut-off points for AHtR, WHtR, MUAC, WC, and NC can be used to diagnose overweight and obesity in Pakistani children.

  • 17.
    Farghali, Rasha A.
    et al.
    Department of Mathematics, Insurance and Applied Statistics, Faculty of Commerce and Business Administration, Helwan University, Cairo, Egypt.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, Florida, USA.
    Abonazel, Mohamed R.
    Department of Applied Statistics and Econometrics, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt.
    Generalized two-parameter estimators in the multinomial logit regression model: methods, simulation and application2023In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 52, no 7, p. 3327-3342Article in journal (Refereed)
    Abstract [en]

    In this article, we propose generalized two-parameter (GTP) estimators and an algorithm for the estimation of shrinkage parameters to combat multicollinearity in the multinomial logit regression model. In addition, the mean squared error properties of the estimators are derived. A simulation study is conducted to investigate the performance of proposed estimators for different sample sizes, degrees of multicollinearity, and the number of explanatory variables. Swedish football league dataset is analyzed to show the benefits of the GTP estimators over the traditional maximum likelihood estimator (MLE). The empirical results of this article revealed that GTP estimators have a smaller mean squared error than the MLE and can be recommended for practitioners.

  • 18.
    Irum, S.
    et al.
    Department of Zoology, University of Gujrat, Gujrat, Pakistan.
    Aftab, M.
    Department of Zoology, University of Gujrat, Gujrat, Pakistan.
    Khan, A.
    Department of Biosciences, COMSATS University Islamabad (CUI), Park Road, Chak Shahzad, Islamabad, Pakistan.
    Naz, S.
    Department of Biological Sciences, National University of Medical Sciences (NUMS), Rawalpindi, Pakistan.
    Simsek, S.
    Department of Parasitology, Faculty of Veterinary Medicine, Firat University, Elazig, Turkey.
    Habib, A.
    Department of Biosciences, COMSATS University Islamabad (CUI), Park Road, Chak Shahzad, Islamabad, Pakistan.
    Afzal, M. S.
    Department of Life Sciences, School of Science, University of Management & Technology (UMT), Lahore, Pakistan.
    Nadeem, M. A.
    Department of Life Sciences, School of Science, University of Management & Technology (UMT), Lahore, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Ahmed, H.
    Department of Biosciences, COMSATS University Islamabad (CUI), Park Road, Chak Shahzad, Islamabad, Pakistan.
    Cutaneous Leishmaniasis (CL): A Cross-Sectional Community Based Survey on Knowledge, Attitude and Practices in a Highly Endemic Area of Waziristan (KPK Province), Pakistan2021In: Acta Tropica, ISSN 0001-706X, E-ISSN 1873-6254, Vol. 213, article id 105746Article in journal (Refereed)
    Abstract [en]

    Recent outbreaks of Cutaneous Leishmaniasis (CL) in Waziristan make the disease a public health concern in Khyber Pakhtunkhwa (KPK) province, Pakistan. The awareness and behavior of local community towards the disease is an important factor towards effective control and management of CL in endemic areas of Pakistan. A cross-sectional community based survey was piloted in new emerging district of North Waziristan Agency (KPK province), Pakistan from August 2019- February 2020. The study aimed to examine the Knowledge, Attitude and Practices (KAP) of the local community members regarding CL. The results revealed that majority of the participants were male. Only 48.2% participants have knowledge about CL and the respondents had a moderate knowledge of CL vector and the disease. Few of the respondents were aware that CL is caused by sand flies, their breeding place, biting time, transmission of CL and control measures. Skin infection and sand-flies were the main disease symptoms and disease vector were known to some of the respondents. Most of the respondents showed positive attitude towards disease seriousness and believed that the disease could be cured and can be treated through modern medicines. Admission to hospitals, cleanliness and use of bed nets were the treatment measures for the disease in suspected patients, whereas some believed that the use of bed nets could be helpful in preventing the leishmaniasis. Moderate knowledge of the CL and its transmission in the study area emphasize the need to initiate health education and awareness campaigns to reduce the disease risk and burden in this highly endemic area in near future.

  • 19.
    Jabbar, Abdul
    et al.
    Department of Clinical Medicine, University of Veterinary and Animal Sciences, Lahore, Punjab, Pakistan.
    Saleem, Muhammad H.
    Department of Clinical Medicine, University of Veterinary and Animal Sciences, Lahore, Punjab, Pakistan.
    Iqbal, Muhammad Z.
    Department of Clinical Medicine, University of Veterinary and Animal Sciences, Lahore, Punjab, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Ashraf, Muhammad
    Department of Theriogenology, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Tolba, Mahmoud M.
    Biomedical Informatics and Biotechnology Group, Department of Informatics and Systems, Division of Engineering research, National Research Centre, Cairo, Egypt.
    Nasser, Hebatallah A.
    Department of Microbiology and Public Health, Faculty of Pharmacy, Helipolis University, Cairo, Egypt.
    Sajjad, Hira
    Department of Food Science and Human Nutrition, University of Veterinary and Animal Sciences, Lahore, Punjab, Pakistan.
    Hassan, Ayesha
    Department of Surgery and Pet sciences, University of Veterinary and Animal Sciences, Lahore, Punjab, Pakistan.
    Imran, Muhammad
    Institute of Biochemistry and Biotechnology, University of Veterinary and Animal Sciences, Lahore, Punjab, Pakistan.
    Ahmad, Imtiaz
    Department of Veterinary Clinical Sciences, University of Poonch Rawalakot, Azad Jammu and Kashmir, Pakistan.
    Epidemiology and antibiogram of common mastitis-causing bacteria in Beetal goats2020In: Veterinary World, ISSN 0972-8988, E-ISSN 2231-0916, Vol. 13, no 12, p. 2596-2607Article in journal (Refereed)
    Abstract [en]

    Background and Aim: Mastitis has been identified as the most prevalent and economically imperative disease among dairy animals. Thus, understanding its common bacterial pathogens and risk factors is necessary to improve udder health at herd, region, or country level. However, scientific research on caprine mastitis, especially on Beetal breed, has remained to be insufficient in Pakistan. Therefore, this study aimed to evaluate the epidemiology and antibiogram assay of common mastitis-causing bacterial agents, that is, Staphylococcus, Streptococcus, and Escherichia coli, in dairy goats.

    Materials and Methods: In total, 500 Beetal goats, irrespective of age and those that were not treated with any kind of antimicrobial agents during the past 120 h, were screened using California Mastitis Test in Pattoki, Kasur District, whereas epidemiological factors were recorded. The milk samples of mastitic goats were then collected and processed using standard methods. Each sample was primarily cultured on nutrient agar. Using a specific medium, each bacterial colony was separated using several streak methods. Six antibiotic disks belonging to different antibiotic groups were used for antibiogram profiling of bacterial isolates. Chi-square test was used to assess the association of baseline characteristics and mastitis occurrence. Meanwhile, multivariable logistic regression (p<0.001) was utilized to determine the risk factors associated with positive and negative dichotomous outcome of mastitis.

    Results: The results revealed that the overall prevalence of goat mastitis was 309 (61.8%), in which 260 (52%) and 49 (9.8%) cases were positive for subclinical mastitis (SCM) and clinical mastitis (CM), respectively. Streptococcus and E. coli were found to be the predominant isolates causing SCM and CM, respectively (p<0.001). It was observed that amoxicillin+clavulanic acid was highly sensitive to isolates of Staphylococcus and Streptococcus and ceftiofur sodium to isolates of Streptococcus and E. coli, while enrofloxacin was found to be sensitive to isolates of Streptococcus and E. coli. Risk factors such as herd structure, deworming, vaccination, presence of ticks, use of teat dip and mineral supplements, feeding type, age, parity, housing, blood in the milk, milk leakage, milk taste, and milk yield were found to have the strongest association with mastitis occurrence, while ease of milking has moderate association.

    Conclusion: In the area examined, cases of SCM were found to be higher compared with that of CM, and ceftiofur sodium has been identified as the preferred treatment in both clinical and subclinical forms of caprine mastitis in Beetal goats. Risk factors for mastitis that was identified in this study can form the basis for the creation of an udder health control program specific for dairy goats. We hope our findings could raise awareness of the risk factors and treatment approaches for common mastitis-causing bacterial agents. 

  • 20.
    Kausar, Tehzeeb
    et al.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Akbar, Atif
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Influence diagnostics for the Cox proportional hazards regression model: method, simulation and applications2023In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 93, no 10, p. 1580-1600Article in journal (Refereed)
    Abstract [en]

    This article investigates the performance of several residuals for the Cox proportional hazards regression model to diagnose the influential observations. The standardized and adjusted forms of residuals are proposed for Cox proportional hazards regression model. In addition, Cook's distance is proposed for both standardized and adjusted residuals. The assessment of different residuals for the identification of influential observations is made through the Monte Carlo simulation. A real dataset of bone marrow transplant Leukaemia is analyzed to show the benefit of the proposed methods. Simulation and application results show that the standardized and adjusted residuals based on the Cox-Snell method perform best for the detection of influential points. Furthermore, the standardized, and adjusted Martingale and deviance residuals work better when the sample size is large.

  • 21.
    Khan, Aisha
    et al.
    Department of Biosciences, COMSATS University Islamabad (CUI), Park Road, Chak Shahzad, Islamabad, Pakistan.
    Sajid, Rawan
    Department of Biosciences, COMSATS University Islamabad (CUI), Park Road, Chak Shahzad, Islamabad, Pakistan.
    Gul, Shaista
    Department of histopathology, Bolan Medical Complex Hospital, Quetta, Pakistan.
    Hussain, Ashiq
    Department of Microbiology, Bolan Medical Complex Hospital, Quetta, Pakistan.
    Zehri, Mohammad T.
    Department of Microbiology, Bolan Medical Complex Hospital, Quetta, Pakistan.
    Naz, Shumaila
    Department of Biological Sciences, National University of Medical Sciences (NUMS), Rawalpindi, Pakistan.
    Simsek, Sami
    Department of Parasitology, Firat University, Elazig, Turkey.
    Waseem, Shahid
    Alpha Genomics Pvt. Ltd. Plot 4-C, Danyal Plaza, Block A, Main Double Road, PWD, Islamabad, Pakistan.
    Afzal, Muhammad S.
    Department of Life Sciences, University of Management & Technology (UMT), Lahore, Pakistan.
    Naqvi, Syed K. U. H.
    Department of Biosciences, COMSATS University Islamabad (CUI), Park Road, Chak Shahzad, Islamabad, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Ahmed, H.
    Epidemiological and Pathological Characteristics of Cutaneous Leishmaniasis from Baluchistan Province of Pakistan2021In: Parasitology, ISSN 0031-1820, E-ISSN 1469-8161, Vol. 148, no 5, p. 591-597Article in journal (Refereed)
    Abstract [en]

    Cutaneous Leishmaniasis (CL) is considered a neglected tropical disease which in Pakistan can now be considered as growing public health problem. The exact figures on the magnitude of disease are lacking both at national and regional level and only a few health centers are available for diagnosis of CL. The present study was designed to identify the epidemiology of CL infection from August 2018 to December 2019 and to assess clinical aspects of CL in Baluchistan Province of Pakistan. A total of 4072 clinically suspected CL cases were analysed statistically. The highest number of CL cases were reported in May, followed by April, January and then July, February, and June and lowest number of cases were observed in March and November. The highest prevalence rate was found in males where 38% of reported cases were aged 0-9 years. The majority (24.4%) of lesions were found on the hands followed by the face in which cheeks, ears and nose were the effected organs. About 50% of the participants have single lesion while 14% of the participants had two and nearly 3% of the participants have six lesions. The atypical clinical presentations were observed in Baluchistan and common unusual presentations were lupus erythematosus. The study findings suggest that more epidemiological studies and health education campaigns are needed for the population awareness regarding CL in Baluchistan. It is recommended that risk factors should be evaluated to establish the control and management strategies to prevent disease at individual and community level. 

  • 22.
    Mushtaq, Aqsa
    et al.
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Shoukat, Tehniat
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Mumtaz, Tanzila
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Ajmal, Kiran
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Fatima, Nayab
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Khan, Aisha
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Kouser, Misbah
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Hussain, Nazeer
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Khan, Sadia Selim
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Afzal, Mohammed Sohail
    Univ Management & Technol UMT, Fac Sci, Dept Life Sci, Lahore, Pakistan..
    Simsek, Sami
    Firat Univ, Fac Vet Med, Dept Parasitol, TR-23119 Elazig, Turkey..
    Ahmed, Haroon
    COMSATS Univ Islamabad CUI, Dept Biosci, Infect Dis Div, Islamabad, Pakistan..
    Tick-borne Diseases in Sheep and Goats in Pakistan: A Systematic Review and Meta-analysis2021In: Acta Parasitologica, ISSN 1230-2821, E-ISSN 1896-1851, Vol. 66, p. 1316-1325Article in journal (Refereed)
    Abstract [en]

    Background

    Ticks are blood-sucking ectoparasites and transmit various types of protozoal, bacterial, and viral diseases in a wild as well as domestic animals and humans globally. Only a few published reports are avaliable on the prevalence of tick-borne diseases in sheep and goats in Pakistan.

    Aim and objective

    The aim of this systematic review and meta-analysis was to evaluate the prevalence (2000-2020) of tick-borne disease (theileriosis, babesiosis, Crimean-Congo hemorrhagic fever infection, and anaplasmosis) in sheep and goats in Pakistan.

    Methods

    A systematic review of articles published in English language (since 2000-2020) was conducted using PubMed and Google Scholar. Diagnostic methods used in the original reference articles were PCR, PCR-RLB, microscopy, and ELISA.

    Results

    The overall prevalence of theileriosis, babesiosis, anaplasmosis, and Crimean-Congo hemorrhagic fever (CCHF) infections was 15.40%, 21.18%, 26.78%, and 11.62%, respectively. The prevalence of anaplasmosis was 22.06% (90/408) in sheep, 21.11% (76/360) in goats, and 40% (120/300) in both sheep and goats with substantial differences (P < 0.001). The prevalence of babesiosis among sheep was 29.88% (104/348) with highly significant differences (P < 0.001), in goats was 29.88% (25/60) with slightly significant differences (P < 0.031%), and in both sheep and goats were 7% (21/300) with highly significant differences (P < 0.001) according to subgroup analysis. The percentage of prevalence of theileriosis was 17.70% (207/1169) in sheep with highly substantial differences (P < 0.001), 4.51% (31/687) in goats with significant differences (P < 0.133), and 25% (125/500) in both sheep and goats with a significant difference among them (P < 0.001). The prevalence of CCHF among sheep was 18.63% (149/800) and 4.63% (37/800) in goats, respectively. The widely used detection method was microscopy (45.56%) followed by ELISA (38.38%), PCR (12.56%), and PCR-RLB (3.48%) test, respectively. This is a comprehensive report on the status of tick-borne disease in sheep and goats in Pakistan.

    Conclusion

    Based on our results, among tick-borne diseases anaplasmosis had the highest prevalence rate in sheep and goats. Due to its high prevalence, control measures should be taken to diagnose and prevent it.

  • 23.
    Naveed, Khalid
    et al.
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Afzal, Saima
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    New shrinkage parameters for the inverse Gaussian Liu regression2022In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 51, no 10, p. 3216-3236Article in journal (Refereed)
    Abstract [en]

    In the Inverse Gaussian Regression (IGR), there is a significant increase in the variance of the commonly used Maximum Likelihood (ML) estimator in the presence of multicollinearity. Alternatively, we suggested the Liu Estimator (LE) for the IGR that is the generalization of Liu. In addition, some estimation methods are proposed to estimate the optimal value of the Liu shrinkage parameter, d. We investigate the performance of these methods by means of Monte Carlo Simulation and a real-life application where Mean Squared Error (MSE) and Mean Absolute Error (MAE) are considered as performance criteria. Simulation and application results show the superiority of new shrinkage parameters to the ML estimator under certain condition.

  • 24.
    Omer, Talha
    et al.
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Hussein, Z.
    Qasim, Muhammad
    Department of Statistics and Computer Science, University of Veterinary and Animal sciences, Lahore, Pakistan.
    Optimized monitoring network of Pakistan2019Conference paper (Refereed)
  • 25.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A weighted average limited information maximum likelihood estimator2023In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798Article in journal (Refereed)
    Abstract [en]

    In this article, a Stein-type weighted limited information maximum likelihood (LIML) estimator is proposed. It is based on a weighted average of the ordinary least squares (OLS) and LIML estimators, with weights inversely proportional to the Hausman test statistic. The asymptotic distribution of the proposed estimator is derived by means of local-to-exogenous asymptotic theory. In addition, the asymptotic risk of the Stein-type LIML estimator is calculated, and it is shown that the risk is strictly smaller than the risk of the LIML under certain conditions. A Monte Carlo simulation and an empirical application of a green patent dataset from Nordic countries are used to demonstrate the superiority of the Stein-type LIML estimator to the OLS, two-stage least squares, LIML and combined estimators when the number of instruments is large.

  • 26.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. Jonkoping Univ, Jonkoping Int Business Sch, Dept Econ Finance & Stat, Jonkoping, Sweden..
    Akram, Muhammad Nauman
    Univ Sargodha, Dept Stat, Sargodha, Pakistan..
    Amin, Muhammad
    Univ Sargodha, Dept Stat, Sargodha, Pakistan..
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Jönköping University, Jönköping International Business School, JIBS, Centre for Entrepreneurship and Spatial Economics (CEnSE).
    A restricted gamma ridge regression estimator combining the gamma ridge regression and the restricted maximum likelihood methods of estimation2022In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 92, no 8, p. 1696-1713Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a restricted gamma ridge regression estimator (RGRRE) by combining the gamma ridge regression (GRR) and restricted maximum likelihood estimator (RMLE) to combat multicollinearity problem for estimating the parameter beta in the gamma regression model. The properties of the new estimator are discussed, and its superiority over the GRR, RMLE and traditional maximum likelihood estimator is theoretically analysed under different conditions. We also suggest some estimating methods to find the optimal value of the shrinkage parameter. A Monte Carlo simulation study is conducted to judge the performance of the proposed estimator. Finally, an empirical application is analysed to show the benefit of RGRRE over the existing estimators.

  • 27.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Amin, M.
    Department of Statistics, University of Sargodha, Pakistan.
    Akram, M. N.
    Department of Statistics, University of Sargodha, Pakistan.
    Omer, Talha
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Hussain, F.
    Department of Animal Nutrition, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Forecasting buffalo population of Pakistan using autoregressive integrated moving average (ARIMA) time series models2019In: Proceedings of the Pakistan Academy of Sciences: Part A, ISSN 2518-4245, Vol. 56, no 3, p. 11-20Article in journal (Refereed)
    Abstract [en]

    Livestock plays a vital role in Pakistan’s economy. Buffalo is the primary source of milk and meat, which is a basic need for human health. So, there is a need to forecast the buffalo population of Pakistan. The main objective of the current study is to determine an appropriate empirical model for forecasting buffalo population of Pakistan to assess its future trend up to the year 2030. We apply different Autoregressive Integrated Moving Average (ARIMA) models on the buffalo population-based on fifty-years’ time-series dataset. Different model selection criteria are used to test the reliability of the ARIMA models. Based on these criteria, we perceive that ARIMA (1, 0, 0) is a more suitable model. Moreover, we also test the fitted model assumptions, such as normality and independence, to find out more accurate forecasted values. This study revealed that the buffalo population expected to increase 30% up to the year 2030 under the assumption that there is no irregular trend can be encountered during forecasted years.

  • 28.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Amin, M.
    Department of Statistics, University of Sargodha, Pakistan.
    Sarwar, M. K. S.
    National Institute for Biotechnology & Genetic Engineering, Faisalabad, Pakistan.
    Effect Of Different Biochemical Traits On Seed Cotton Yield: An Application Of Liu Linear Regression2020In: Journal of Animal and Plant Sciences, ISSN 1018-7081, Vol. 30, no 6, p. 1533-1539Article in journal (Refereed)
    Abstract [en]

    This article aimed to study the associations among the biochemical traits and their effects on seed cotton yield using the regression analysis and to assess the alternative approach for reducing the impact of multicollinearity problem in estimating the regression coefficients. The field experiment was conducted where five explanatory variables (chlorophyll 'a', chlorophyll 'b', total chlorophyll, total soluble protein and total soluble sugar) and one dependent variable seed cotton yield were measured. The correlation matrix of X'X showed that biochemical traits were significantly correlated. The multicollinearity problem among the biochemical traits was determined by condition index and correlation matrix. Using the least square regression analysis, the effects of biochemical traits on seed cotton yield were not satisfactory since least square regression model has high value of MSE (3352475), AIC (366.7) and inconsistent estimates of traits. The Liu regression analysis was efficient (MSE = 57212 and AIC = 363.8) and reliable in reducing the adverse effects of multicollinearity. The Liu regression results indicated that total chlorophyll and total soluble protein were contributed a significant (P <= 0.05) role in seed cotton yield. In contrast, ordinary least square regression analysis was showed insignificant (P > 0.05) effect of total chlorophyll on seed cotton yield.

  • 29.
    Qasim, Muhammad
    et al.
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amanullah, Muhammad
    Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
    On the performance of some new Liu parameters for the gamma regression model2018In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 88, no 16, p. 3065-3080Article in journal (Refereed)
    Abstract [en]

    The maximum likelihood (ML) method is used to estimate the unknown Gamma regression (GR) coefficients. In the presence of multicollinearity, the variance of the ML method becomes overstated and the inference based on the ML method may not be trustworthy. To combat multicollinearity, the Liu estimator has been used. In this estimator, estimation of the Liu parameter d is an important problem. A few estimation methods are available in the literature for estimating such a parameter. This study has considered some of these methods and also proposed some new methods for estimation of the d. The Monte Carlo simulation study has been conducted to assess the performance of the proposed methods where the mean squared error (MSE) is considered as a performance criterion. Based on the Monte Carlo simulation and application results, it is shown that the Liu estimator is always superior to the ML and recommendation about which best Liu parameter should be used in the Liu estimator for the GR model is given. 

  • 30.
    Qasim, Muhammad
    et al.
    Department of Statistics and Computer Science, University of Veterinary and Animal sciences, Lahore, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Azam, Muhammad
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Omer, Talha
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    On Almost Unbiased Ridge Estimator in the Poisson Regression Model2019Conference paper (Refereed)
  • 31.
    Qasim, Muhammad
    et al.
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Amin, Muhammad
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Omer, Talha
    Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Performance of some new Liu parameters for the linear regression model2020In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 49, no 17, p. 4178-4196Article in journal (Refereed)
    Abstract [en]

    This article introduces some Liu parameters in the linear regression model based on the work of Shukur, Månsson, and Sjölander. These methods of estimating the Liu parameter d increase the efficiency of Liu estimator. The comparison of proposed Liu parameters and available methods has done using Monte Carlo simulation and a real data set where the mean squared error, mean absolute error and interval estimation are considered as performance criterions. The simulation study shows that under certain conditions the proposed Liu parameters perform quite well as compared to the ordinary least squares estimator and other existing Liu parameters. 

  • 32.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Awan, U. A.
    Department of Medical Laboratory Technology, University of Haripur, Haripur, Khyber Pakhtunkhwa (22620), Pakistan.
    Afzal, M. S.
    Department of Medical Laboratory Technology, University of Haripur, Haripur, Khyber Pakhtunkhwa (22620), Pakistan.
    Saqib, M. A. N.
    Department of Medical Laboratory Technology, University of Haripur, Haripur, Khyber Pakhtunkhwa (22620), Pakistan.
    Siddiqui, S.
    Department of Medicine, Pakistan Institute of Medical Sciences, Islamabad.
    Ahmed, H.
    Department of Biosciences, COMSATS University Islamabad (CUI), Islamabad, Pakistan.
    Dataset of knowledge, attitude, practices and psychological implications of healthcare workers in Pakistan during COVID-19 pandemic2020In: Data in Brief, E-ISSN 2352-3409, Vol. 32, article id 106234Article in journal (Refereed)
    Abstract [en]

    The COVID-19 pandemic has created a global health emergency and has a huge impact on the health care workers, especially on their mental health. The dataset presented was an assessment of COVID-19 related knowledge, attitude, practices and its effects on the mental health of frontline healthcare workers in Pakistan. The data were collected using a snowball sampling technique. A questionnaire was developed assessing sociodemographic characteristics (6 items), knowledge (11 items), attitude (5 items), practices (6 items), information sources (1 item) and psychological implications (12 items) and distributed using online tools. The dataset includes 476 healthcare workers in Pakistan. The dataset will help to prevent and curb the spread of COVID-19 among health workers and contribute to policymakers. Furthermore, our dataset provides detailed insights into different risk factors of psychological problems, and it may be served as the reference for various in-depth surveys.

  • 33.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. G.
    Department of Mathematics and Statistics, Florida International University, Miami, FL, USA.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    A new Poisson Liu Regression Estimator: method and application2020In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 47, no 12, p. 2258-2271Article in journal (Refereed)
    Abstract [en]

    This paper considers the estimation of parameters for the Poisson regression model in the presence of high, but imperfect multicollinearity. To mitigate this problem, we suggest using the Poisson Liu Regression Estimator (PLRE) and propose some new approaches to estimate this shrinkage parameter. The small sample statistical properties of these estimators are systematically scrutinized using Monte Carlo simulations. To evaluate the performance of these estimators, we assess the Mean Square Errors (MSE) and the Mean Absolute Percentage Errors (MAPE). The simulation results clearly illustrate the benefit of the methods of estimating these types of shrinkage parameters in finite samples. Finally, we illustrate the empirical relevance of our newly proposed methods using an empirically relevant application. Thus, in summary, via simulations of empirically relevant parameter values, and by a standard empirical application, it is clearly demonstrated that our technique exhibits more precise estimators, compared to traditional techniques - at least when multicollinearity exist among the regressors.

  • 34.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Amin, Muhammad
    Univ Sargodha, Dept Stat, Sargodha, Pakistan.
    Golam Kibria, B. M.
    Florida Int Univ, Dept Math & Stat, Miami, FL, USA.
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Jönköping University, Jönköping International Business School, JIBS, Economics.
    Biased Adjusted Poisson Ridge Estimators-Method and Application2020In: Iranian Journal of Science and Technology Transaction A: Science, ISSN 1028-6276, Vol. 44, p. 1775-1789Article in journal (Refereed)
    Abstract [en]

    Mansson and Shukur (Econ Model 28:1475-1481, 2011) proposed a Poisson ridge regression estimator (PRRE) to reduce the negative effects of multicollinearity. However, a weakness of the PRRE is its relatively large bias. Therefore, as a remedy, Turkan and Ozel (J Appl Stat 43:1892-1905, 2016) examined the performance of almost unbiased ridge estimators for the Poisson regression model. These estimators will not only reduce the consequences of multicollinearity but also decrease the bias of PRRE and thus perform more efficiently. The aim of this paper is twofold. Firstly, to derive the mean square error properties of the Modified Almost Unbiased PRRE (MAUPRRE) and Almost Unbiased PRRE (AUPRRE) and then propose new ridge estimators for MAUPRRE and AUPRRE. Secondly, to compare the performance of the MAUPRRE with the AUPRRE, PRRE and maximum likelihood estimator. Using both simulation study and real-world dataset from the Swedish football league, it is evidenced that one of the proposed, MAUPRRE ((k) over cap (q4)) performed better than the rest in the presence of high to strong (0.80-0.99) multicollinearity situation.

  • 35.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Golam Kibria, B. M. Golam
    Department of Mathematics and Statistics, Florida International University, Miami, FL, United States.
    On some beta ridge regression estimators: method, simulation and application2021In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 91, no 9, p. 1699-1712Article in journal (Refereed)
    Abstract [en]

    The classic statistical method for modelling the rates and proportions is the beta regression model (BRM). The standard maximum likelihood estimator (MLE) is used to estimate the coefficients of the BRM. However, this MLE is very sensitive when the regressors are linearly correlated. Therefore, this study introduces a new beta ridge regression (BRR) estimator as a remedy to the problem of instability of the MLE. We study the mean squared error properties of the BRR estimator analytically and then based on the derived MSE, we suggest some new estimators of the shrinkage parameter. We also suggest a median squared error (SE) performance criterion, which can be used to achieve strong evidence in favour of the proposed method for the Monte Carlo simulation study. The performance of BRR and MLE is appraised through Monte Carlo simulation. Finally, an empirical application is used to show the advantages of the proposed estimator.

  • 36.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Jönköping University, Jönköping International Business School, JIBS, Centre for Entrepreneurship and Spatial Economics (CEnSE).
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Economics.
    Kibria, B. M. G.
    Department of Mathematics and Statistics, Florida International University, Miami, FL, United States.
    A new class of efficient and debiased two-step shrinkage estimators: method and application2022In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 49, no 16, p. 4181-4205Article in journal (Refereed)
    Abstract [en]

    This paper introduces a new class of efficient and debiased two-step shrinkage estimators for a linear regression model in the presence of multicollinearity. We derive the proposed estimators’ mean square error and define the necessary and sufficient conditions for superiority over the existing estimators. In addition, we develop an algorithm for selecting the shrinkage parameters for the proposed estimators. The comparison of the new estimators versus the traditional ordinary least squares, ridge regression, Liu, and the two-parameter estimators is done by a matrix mean square error criterion. The Monte Carlo simulation results show the superiority of the proposed estimators under certain conditions. In the presence of high but imperfect multicollinearity, the two-step shrinkage estimators’ performance is relatively better. Finally, two real-world chemical data are analyzed to demonstrate the advantages and the empirical relevance of our newly proposed estimators. It is shown that the standard errors and the estimated mean square error decrease substantially for the proposed estimator. Hence, the precision of the estimated parameters is increased, which of course is one of the main objectives of the practitioners.

  • 37.
    Qasim, Muhammad
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Månsson, Kristofer
    Jönköping University, Jönköping International Business School, JIBS, Statistics. Jönköping University, Jönköping International Business School, JIBS, Centre for Entrepreneurship and Spatial Economics (CEnSE).
    Sjölander, Pär
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Kibria, B. M. G.
    Department of mathematics and statistics, Florida international university, Miami, FL, United States.
    Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity2024In: Econometrics and Statistics, ISSN 2452-3062Article in journal (Refereed)
    Abstract [en]

    A Stein-type control function maximum likelihood (CFML) estimator is suggested for the probit model in the presence of endogeneity. This novel estimator combines the probit maximum likelihood and CFML estimators. The asymptotic distribution and risk function for the new estimator is derived. It is demonstrated that, subject to certain conditions of the shrinkage parameter, the asymptotic risk of the new estimator is strictly smaller than the risk of the CFML. Monte Carlo simulations illustrate the method's superiority in finite samples. The method is also applied to analyze the impact of managerial incentives on the use of foreign-exchange derivatives.

  • 38.
    Saqib, M. A. N.
    et al.
    Pakistan Health Research Council, Head Office, Islamabad, Pakistan.
    Siddiqui, S.
    Department of Medicine, Pakistan Institute of Medical Sciences, Islamabad, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Jamil, M. A.
    Department of Medicine, Pakistan Institute of Medical Sciences, Islamabad, Pakistan.
    Rafique, I.
    Pakistan Health Research Council, Head Office, Islamabad, Pakistan.
    Awan, U. A.
    Department of Life Sciences, University of Management & Technology (UMT), Lahore, Pakistan.
    Haroon, M.
    Department of Biosciences, COMSATS University Islamabad (CUI), Islamabad, Pakistan.
    Afzal, M. S.
    Department of Life Sciences, University of Management & Technology (UMT), Lahore, Pakistan.
    Effect of COVID-19 lockdown on patients with chronic diseases2020In: Diabetes & Metabolic syndrome: clinical Research & Reviews, ISSN 1871-4021, E-ISSN 1878-0334, Vol. 14, no 6, p. 1621-1623Article in journal (Other academic)
    Abstract [en]

    Background and aims: We sought to measure the effect of lockdown, implemented to contain COVID-19 infection, on routine living and health of patients with chronic diseases and challenges faced by them. Methods: A semi-structured online questionnaire was generated using “Google forms” and sent to the patients with chronic diseases using WhatsApp. Data were retrieved and analyzed using SPSS. Results: Out of 181 participants, 98% reported effect of lockdown on their routine living while 45% reported an effect on their health. The key challenges due to lockdown were to do daily exercise, missed routine checkup/lab testing and daily health care. Conclusion: It is important to strategize the plan for patients with chronic diseases during pandemic or lockdown.

  • 39.
    Toker, Selma
    et al.
    Department of Statistics, Cukurova University, Adana, Turkey.
    Üstündag Siray, Gülesen
    Department of Statistics, Cukurova University, Adana, Turkey.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Developing a First Order Two Parameter Estimator for Generalized Linear Models2019In: 11th International statistics Congress ISC2019, Turkish Statistical Association and Giresun University , 2019Conference paper (Refereed)
    Abstract [en]

    The generalized linear models were defined by Nelder and Wedderburn (1972) and these models allow us to fit regression models for univariate response data which follow a very common exponential family of distribution. The unknown regression coefficients of the generalized linear models are estimated by the maximum likelihood estimator. However, in the existence of multicollinearity, the variance of the maximum likelihood estimator becomes inflated and the statistical inferences based on the maximum likelihood method may not be reliable. In this study, we develop a first order two parameter estimator which combines the advantages of ridge and contraction estimators in the generalized linear models by extending the work of Özkale and Kaçıranlar (2007) in the linear model. The superiority of the first order two parameter estimator to the maximum likelihood, ridge and Liu estimators is investigated with regard to the mean square error criterion. We also examine some optimal estimators of biasing parameters. In addition to the theoretical comparisons, the performance of the estimators is judged by numerical evaluations where the mean square error is considered as a performance criterion.

  • 40.
    Yasin, A.
    et al.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Amin, M.
    Department of Statistics, University of Sargodha, Sargodha, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Statistics.
    Muse, A. H.
    Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya.
    Soliman, A. B.
    Pan African University Institute of Basic Sciences Technology and Innovation (PAUSTI), Nairobi, Kenya.
    More on the Ridge Parameter Estimators for the Gamma Ridge Regression Model: Simulation and Applications2022In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, article id 6769421Article in journal (Refereed)
    Abstract [en]

    The Gamma ridge regression estimator (GRRE) is commonly used to solve the problem of multicollinearity, when the response variable follows the gamma distribution. Estimation of the ridge parameter estimator is an important issue in the GRRE as well as for other models. Numerous ridge parameter estimators are proposed for the linear and other regression models. So, in this study, we generalized these estimators for the Gamma ridge regression model. A Monte Carlo simulation study and two real-life applications are carried out to evaluate the performance of the proposed ridge regression estimators and then compared with the maximum likelihood method and some existing ridge regression estimators. Based on the simulation study and real-life applications results, we suggest some better choices of the ridge regression estimators for practitioners by applying the Gamma regression model with correlated explanatory variables. 

  • 41.
    Yasin, M.
    et al.
    Sugarcane Research Institute, Ayub Agricultural Research Institute, Faisalabad, Pakistan.
    Ahmad, A.
    Asian Disaster Preparedness Centre (ADPC), Islamabad, Pakistan.
    Khaliq, T.
    Agro-Climatology Lab, Department of Agronomy, University of Agriculture, Faisalabad, Pakistan.
    Habib-ur-Rahman, M.
    Institute of Crop Science and Resource Conservation (INRES), University Bonn, Bonn, 53115, Germany.
    Niaz, S.
    Sugarcane Research Institute, Ayub Agricultural Research Institute, Faisalabad, Pakistan.
    Gaiser, T.
    Institute of Crop Science and Resource Conservation (INRES), University Bonn, Bonn, 53115, Germany.
    Ghafoor, I.
    Department of Agronomy, MNS-University of Agriculture Multan, Multan, 60650, Pakistan.
    Hassan, H. S.
    Sera Processing Lab, National Institute of Health, Islamabad, Pakistan.
    Qasim, Muhammad
    Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics.
    Hoogenboom, G.
    Institute for Sustainable Food Systems, University of Florida, 184 Rogers Hall, Gainesville, 32611, FL, United State.
    Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models2022In: Environmental Science and Pollution Research, ISSN 0944-1344, E-ISSN 1614-7499, Vol. 29, p. 18967-18988Article in journal (Refereed)
    Abstract [en]

    Future climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer’s fields for ten sowing dates during two consecutive growing years. Data about phenology, morphology, biomass development, and yield were recorded by adopting standard procedures and protocols. The CSM-CERES, APSIM, and CSM-IXIM-Maize models were calibrated and evaluated. Five GCMs among 29 were selected based on classification into different groups and uncertainty to predict climatic changes in the future. The results predicted that there would be a rise in temperature (1.57–3.29 °C) during the maize growing season in five General Circulation Models (GCMs) by using RCP 8.5 scenarios for the mid-century (2040–2069) as compared with the baseline (1980–2015). The CERES-Maize and APSIM-Maize model showed lower root mean square error values (2.78 and 5.41), higher d-index (0.85 and 0.87) along reliable R2 (0.89 and 0.89), respectively for days to anthesis and maturity, while the CSM-IXIM-Maize model performed well for growth parameters (leaf area index, total dry matter) and yield with reasonably good statistical indices. The CSM-IXIM-Maize model performed well for all hybrids during both years whereas climate models, NorESM1-M and IPSL-CM5A-MR, showed less uncertain results for climate change impacts. Maize models along GCMs predicted a reduction in yield (8–55%) than baseline. Maize crop may face a high yield decline that could be overcome by modifying the sowing dates and fertilizer (fertigation) and heat and drought-tolerant hybrids.

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