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Publications (10 of 13) Show all publications
Akram, M. N., Amin, M. & Qasim, M. (2020). A new Liu-type estimator for the Inverse Gaussian Regression Model. Journal of Statistical Computation and Simulation, 90(7), 1153-1172
Open this publication in new window or tab >>A new Liu-type estimator for the Inverse Gaussian Regression Model
2020 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 90, no 7, p. 1153-1172Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2020
Keywords
Inverse Gaussian Regression Model, multicollinearity, maximum likelihood estimator, Liu-type estimator, mean squared error, application of IGRM, GDP, IGRRE, IGLE, IGLTE
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-47710 (URN)10.1080/00949655.2020.1718150 (DOI)000509027000001 ()2-s2.0-85078462163 (Scopus ID);IHHÖvrigtIS (Local ID);IHHÖvrigtIS (Archive number);IHHÖvrigtIS (OAI)
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-05-05Bibliographically approved
Amin, M., Qasim, M., Yasin, A. & Amanullah, M. (2020). Almost unbiased ridge estimator in the gamma regression model. Communications in statistics. Simulation and computation, 1-21
Open this publication in new window or tab >>Almost unbiased ridge estimator in the gamma regression model
2020 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, p. 1-21Article in journal (Refereed) Epub ahead of print
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, 2020
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);IHHÖvrigtIS (Local ID);IHHÖvrigtIS (Archive number);IHHÖvrigtIS (OAI)
Available from: 2020-02-17 Created: 2020-02-17 Last updated: 2020-03-05
Qasim, M., Kibria, B. M., Månsson, K. & Sjölander, P. (2019). A new Poisson Liu Regression Estimator: method and application. Journal of Applied Statistics
Open this publication in new window or tab >>A new Poisson Liu Regression Estimator: method and application
2019 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532Article in journal (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
MLE; MSE; Poisson regression; Liu estimator; shrinkage estimators; simulation study
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-47296 (URN)10.1080/02664763.2019.1707485 (DOI)000504551800001 ()2-s2.0-85077385521 (Scopus ID);IHHÖvrigtIS (Local ID);IHHÖvrigtIS (Archive number);IHHÖvrigtIS (OAI)
Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-01-13
Toker, S., Üstündag Siray, G. & Qasim, M. (2019). Developing a First Order Two Parameter Estimator for Generalized Linear Models. In: 11th International statistics Congress ISC2019: . Paper presented at 11th International statistics Congress ISC2019, 4 - 8 October 2019, Bodrum, Mugla, Turkey. Turkish Statistical Association and Giresun University
Open this publication in new window or tab >>Developing a First Order Two Parameter Estimator for Generalized Linear Models
2019 (English)In: 11th International statistics Congress ISC2019, Turkish Statistical Association and Giresun University , 2019Conference paper, Published 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.

Place, publisher, year, edition, pages
Turkish Statistical Association and Giresun University, 2019
Keywords
Generalized linear model, two parameter estimator, multicollinearity, first order approximation
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-47706 (URN)
Conference
11th International statistics Congress ISC2019, 4 - 8 October 2019, Bodrum, Mugla, Turkey
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-03Bibliographically approved
Amin, M., Amanullah, M., Aslam, M. & Qasim, M. (2019). Influence diagnostics in gamma ridge regression model. Journal of Statistical Computation and Simulation, 89(3), 536-556
Open this publication in new window or tab >>Influence diagnostics in gamma ridge regression model
2019 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 3, p. 536-556Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
GRM, GRRM, influential observation, multicollinearity, Pearson residuals, ridge estimates
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-46439 (URN)10.1080/00949655.2018.1558226 (DOI)2-s2.0-85058682454 (Scopus ID)
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-03Bibliographically approved
Qasim, M., Amin, M., Azam, M. & Omer, T. (2019). On Almost Unbiased Ridge Estimator in the Poisson Regression Model. In: : . Paper presented at 2nd Asia-Pacific Conference on Applied Mathematics and Statistics, University of Malaya, Kuala Lumpur, Malaysia, February 21-24, 2019.
Open this publication in new window or tab >>On Almost Unbiased Ridge Estimator in the Poisson Regression Model
2019 (English)Conference paper, Published paper (Refereed)
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-46447 (URN)
Conference
2nd Asia-Pacific Conference on Applied Mathematics and Statistics, University of Malaya, Kuala Lumpur, Malaysia, February 21-24, 2019
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-07Bibliographically approved
Omer, T., Hussein, Z. & Qasim, M. (2019). Optimized monitoring network of Pakistan. In: : . Paper presented at 2nd Asia-Pacific Conference on Applied Mathematics and Statistics, University of Malaya, Kuala Lumpur, Malaysia, February 21-24, 2019.
Open this publication in new window or tab >>Optimized monitoring network of Pakistan
2019 (English)Conference paper, Published paper (Refereed)
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-46448 (URN)
Conference
2nd Asia-Pacific Conference on Applied Mathematics and Statistics, University of Malaya, Kuala Lumpur, Malaysia, February 21-24, 2019
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-07Bibliographically approved
Amin, M., Qasim, M. & Amanullah, M. (2019). Performance of Asar and Genç and Huang and Yang’s Two-Parameter Estimation Methods for the Gamma Regression Model. Iranian Journal of Science and Technology, Transactions A: Science, 43(6), 2951-2963
Open this publication in new window or tab >>Performance of Asar and Genç and Huang and Yang’s Two-Parameter Estimation Methods for the Gamma Regression Model
2019 (English)In: Iranian Journal of Science and Technology, Transactions A: Science, ISSN 1028-6276, Vol. 43, no 6, p. 2951-2963Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Gamma regression model, ML estimator, Mean squared error, Multicollinearity, Two-parameter estimator
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-46446 (URN)10.1007/s40995-019-00777-3 (DOI)000511605700025 ()2-s2.0-85073997368 (Scopus ID);IHHÖvrigtIS (Local ID);IHHÖvrigtIS (Archive number);IHHÖvrigtIS (OAI)
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2020-03-27Bibliographically approved
Qasim, M., Amin, M. & Omer, T. (2019). Performance of some new Liu parameters for the linear regression model. Communications in Statistics - Theory and Methods
Open this publication in new window or tab >>Performance of some new Liu parameters for the linear regression model
2019 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArticle in journal (Refereed) Epub ahead of print
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. 

Place, publisher, year, edition, pages
Taylor & Francis, 2019
Keywords
Liu estimator, Liu parameters, mean absolute error, mean squared error, Multicollinearity, Errors, Intelligent systems, Mean square error, Monte Carlo methods, Regression analysis, Parameter estimation
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-46440 (URN)10.1080/03610926.2019.1595654 (DOI)000466204700001 ()2-s2.0-85064660455 (Scopus ID)
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-07
Sattar, T., Ullah, M. I., Qasim, M. & Warraich, I. A. (2019). Role of job designs in determining employees’ work motivation in banking sector of Multan City, Pakistan. Review of Economics and Development Studies, 5(1), 145-154
Open this publication in new window or tab >>Role of job designs in determining employees’ work motivation in banking sector of Multan City, Pakistan
2019 (English)In: Review of Economics and Development Studies, ISSN 2519-9692, Vol. 5, no 1, p. 145-154Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
CSRC Publishing, 2019
Keywords
Employee, Work Motivation, Job Designs, Job Characteristics, Job Enrichment, Job Rotation, Quality of Work Life
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:hj:diva-46445 (URN)10.26710/reads.v5i1.425 (DOI)
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-03Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0279-5305

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