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Publications (10 of 41) Show all publications
Qasim, M., Månsson, K., Sjölander, P. & Kibria, B. M. (2024). Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity. Econometrics and Statistics
Open this publication in new window or tab >>Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity
2024 (English)In: Econometrics and Statistics, ISSN 2452-3062Article in journal (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Control function, Endogeneity, Instrumental variable, Model averaging, Probit Stein estimator
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-63274 (URN)10.1016/j.ecosta.2023.12.001 (DOI)2-s2.0-85181133971 (Scopus ID)HOA;intsam;926131 (Local ID)HOA;intsam;926131 (Archive number)HOA;intsam;926131 (OAI)
Available from: 2024-01-10 Created: 2024-01-10 Last updated: 2024-01-10
Akram, M. N., Amin, M. & Qasim, M. (2023). A new biased estimator for the gamma regression model: Some applications in medical sciences. Communications in Statistics - Theory and Methods, 52(11), 3612-3632
Open this publication in new window or tab >>A new biased estimator for the gamma regression model: Some applications in medical sciences
2023 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 52, no 11, p. 3612-3632Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2023
Keywords
Gamma regression model, James-Stein estimator, MSE, ridge regression, shrinkage estimator, Mean square error, Biased estimators, Evaluation criteria, James-Stein estimators, Mean squared error, Medical dataset, Multicollinearity, Regression model, Simulation studies, Regression analysis
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-54761 (URN)10.1080/03610926.2021.1977958 (DOI)000697403600001 ()2-s2.0-85115273501 (Scopus ID);intsam;54761 (Local ID);intsam;54761 (Archive number);intsam;54761 (OAI)
Available from: 2021-09-28 Created: 2021-09-28 Last updated: 2023-04-21Bibliographically approved
Qasim, M. (2023). A weighted average limited information maximum likelihood estimator. Statistical papers
Open this publication in new window or tab >>A weighted average limited information maximum likelihood estimator
2023 (English)In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798Article in journal (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
2SLS, Endogeneity, Instrumental variables, LIML, Many weak instruments, Shrinkage estimator, Stein estimation
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-62757 (URN)10.1007/s00362-023-01485-2 (DOI)001092166200001 ()2-s2.0-85173791738 (Scopus ID)HOA;;911837 (Local ID)HOA;;911837 (Archive number)HOA;;911837 (OAI)
Available from: 2023-10-23 Created: 2023-10-23 Last updated: 2023-11-28
Farghali, R. A., Qasim, M., Kibria, B. M. & Abonazel, M. R. (2023). Generalized two-parameter estimators in the multinomial logit regression model: methods, simulation and application. Communications in statistics. Simulation and computation, 52(7), 3327-3342
Open this publication in new window or tab >>Generalized two-parameter estimators in the multinomial logit regression model: methods, simulation and application
2023 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 52, no 7, p. 3327-3342Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2023
Keywords
Generalized two-parameter estimators, MSE, Multicollinearity, Multinomial logistic regression, Simulation, Swedish football league
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-53397 (URN)10.1080/03610918.2021.1934023 (DOI)000658970200001 ()2-s2.0-85107597614 (Scopus ID)HOA;intsam;749085 (Local ID)HOA;intsam;749085 (Archive number)HOA;intsam;749085 (OAI)
Available from: 2021-06-18 Created: 2021-06-18 Last updated: 2023-09-05Bibliographically approved
Kausar, T., Akbar, A. & Qasim, M. (2023). Influence diagnostics for the Cox proportional hazards regression model: method, simulation and applications. Journal of Statistical Computation and Simulation, 93(10), 1580-1600
Open this publication in new window or tab >>Influence diagnostics for the Cox proportional hazards regression model: method, simulation and applications
2023 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 93, no 10, p. 1580-1600Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2023
Keywords
Cox proportional hazards model, Cook's distance, Standardized residuals, Adjusted residuals, Cox-Snell residual, Influential observations
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-59220 (URN)10.1080/00949655.2022.2145608 (DOI)000894235500001 ()2-s2.0-85143727455 (Scopus ID)HOA;intsam;850117 (Local ID)HOA;intsam;850117 (Archive number)HOA;intsam;850117 (OAI)
Available from: 2022-12-22 Created: 2022-12-22 Last updated: 2023-09-06Bibliographically approved
Abdel-Rahman, S., Awwad, F. A., Qasim, M. & Abonazel, M. R. (2023). New evidence of gender inequality during COVID-19 outbreak in the Middle East and North Africa. Heliyon, 9(7), Article ID e17705.
Open this publication in new window or tab >>New evidence of gender inequality during COVID-19 outbreak in the Middle East and North Africa
2023 (English)In: Heliyon, E-ISSN 2405-8440, Vol. 9, no 7, article id e17705Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Employment outcomes, Gender gap, Income reductions, Job loss, MENA region, Multivariate probit model
National Category
Economics
Identifiers
urn:nbn:se:hj:diva-62118 (URN)10.1016/j.heliyon.2023.e17705 (DOI)001055540500001 ()37456038 (PubMedID)2-s2.0-85164028799 (Scopus ID)GOA;intsam;895949 (Local ID)GOA;intsam;895949 (Archive number)GOA;intsam;895949 (OAI)
Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2023-09-15Bibliographically approved
Qasim, M., Månsson, K., Sjölander, P. & Kibria, B. M. (2022). A new class of efficient and debiased two-step shrinkage estimators: method and application. Journal of Applied Statistics, 49(16), 4181-4205
Open this publication in new window or tab >>A new class of efficient and debiased two-step shrinkage estimators: method and application
2022 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 49, no 16, p. 4181-4205Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2022
Keywords
chemical structures, Debiased estimator, Monte Carlo simulations, multicollinearity, ridge regression, two-parameter estimator
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-54692 (URN)10.1080/02664763.2021.1973389 (DOI)000695991400001 ()2-s2.0-85114876661 (Scopus ID)HOA;intsam;766666 (Local ID)HOA;intsam;766666 (Archive number)HOA;intsam;766666 (OAI)
Funder
The Research Council of Norway, 274569
Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2023-02-20Bibliographically approved
Qasim, M., Akram, M. N., Amin, M. & Månsson, K. (2022). A restricted gamma ridge regression estimator combining the gamma ridge regression and the restricted maximum likelihood methods of estimation. Journal of Statistical Computation and Simulation, 92(8), 1696-1713
Open this publication in new window or tab >>A restricted gamma ridge regression estimator combining the gamma ridge regression and the restricted maximum likelihood methods of estimation
2022 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 92, no 8, p. 1696-1713Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2022
Keywords
Gamma regression model, maximum likelihood estimator, multicollinearity, mean squared error, restricted gamma ridge regression estimator
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-55264 (URN)10.1080/00949655.2021.2005063 (DOI)000723102000001 ()2-s2.0-85120084796 (Scopus ID)HOA;intsam;781634 (Local ID)HOA;intsam;781634 (Archive number)HOA;intsam;781634 (OAI)
Available from: 2021-12-06 Created: 2021-12-06 Last updated: 2023-02-20Bibliographically approved
Amin, M., Qasim, M., Yasin, A. & Amanullah, M. (2022). Almost unbiased ridge estimator in the gamma regression model. Communications in statistics. Simulation and computation, 51(7), 3830-3850
Open this publication in new window or tab >>Almost unbiased ridge estimator in the gamma regression model
2022 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 51, no 7, p. 3830-3850Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2022
Keywords
Gamma regression, Multicollinearity, Almost unbiased gamma ridge regression, Monte Carlo simulation
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-47798 (URN)10.1080/03610918.2020.1722837 (DOI)000513414300001 ()2-s2.0-85079419471 (Scopus ID);intsam;1393548 (Local ID);intsam;1393548 (Archive number);intsam;1393548 (OAI)
Available from: 2020-02-17 Created: 2020-02-17 Last updated: 2022-08-24Bibliographically approved
Yasin, M., Ahmad, A., Khaliq, T., Habib-ur-Rahman, M., Niaz, S., Gaiser, T., . . . Hoogenboom, G. (2022). Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models. Environmental Science and Pollution Research, 29, 18967-18988
Open this publication in new window or tab >>Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models
Show others...
2022 (English)In: Environmental Science and Pollution Research, ISSN 0944-1344, E-ISSN 1614-7499, Vol. 29, p. 18967-18988Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Adaptation, CERES-Maize, CSM-IXIM, APSIM-Maize, Phenology, Climate variability, LAI, Maize hybrids, Sowing time, Sustainable maize production, TDM, Yield
National Category
Agricultural Science
Identifiers
urn:nbn:se:hj:diva-55067 (URN)10.1007/s11356-021-17050-z (DOI)000712505900013 ()34705205 (PubMedID)2-s2.0-85117905092 (Scopus ID)HOA;intsam;776665 (Local ID)HOA;intsam;776665 (Archive number)HOA;intsam;776665 (OAI)
Available from: 2021-11-15 Created: 2021-11-15 Last updated: 2022-04-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0279-5305

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