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  • 1.
    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, p. 1-20Article 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.

  • 2.
    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. 

  • 3.
    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. 

  • 4.
    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.

  • 5.
    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 model2017In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798Article 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. 

  • 6.
    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 model2020In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, p. 1-21Article 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.

  • 7.
    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)
  • 8.
    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. 

  • 9.
    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)
  • 10.
    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 model2019In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArticle 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. 

  • 11.
    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 application2019In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532Article 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.

  • 12.
    Sattar, Tehmina
    et al.
    Department of Sociology, Bahauddin Zakariya University, Multan, Pakistan.
    Ullah, Muhammad Imdad
    Lyallpur Business School, Government College University, Faisalabad.
    Qasim, Muhammad
    Department of Statistics & Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
    Warraich, Imtiaz Ahmad
    Department of Sociology, Bahauddin Zakariya University, Multan, Pakistan.
    Role of job designs in determining employees’ work motivation in banking sector of Multan City, Pakistan2019In: Review of Economics and Development Studies, ISSN 2519-9692, Vol. 5, no 1, p. 145-154Article in journal (Refereed)
    Abstract [en]

    This article presents theoretical and empirical underpinnings between job designs and employees’ work motivation in banking sector of Multan city, Pakistan. The study adopted a cross-sectional survey research design in which 362 employees participated through simple random sampling technique. The findings of the study revealed that female employees are more motivated towards their jobs than male employees. Moreover, job characteristics and job rotation are high among senior bank employees having experience greater than 12 years. The study concluded that job enrichment is the highest influential factor in determining employees work motivation while quality of work life is negatively influencing their enthusiasm level towards job. In the wake of new technological transformations, academic insight into the current work would further guide the policy makers for designing the jobs for banking sector through decentralization of managerial powers, changing in accordance with the global trends, as well as applying autonomous, mastery oriented and purposely directed policies.

  • 13.
    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.

1 - 13 of 13
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