Qamruz Zaman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Qamruz Zaman has a total of 43 co-authors. Here are the top co-authors by number of shared publications:
- Danish Wasim
Department of Management Sciences, Abasyn University Peshawar
12 shared publications - Bhuiyan Mohammad Golam Kibria
Department of Mathematics and Statistics, Florida International University
8 shared publications - Maha Shabbir
Department of Mathematics and Statistics, Lahore School of Economics
4 shared publications - Mujeeb Hussain
Department of Statistics, Government College Peshawar, Pakistan
4 shared publications - Sidra Nawaz
Department of Statistics, University of Peshawar
4 shared publications - Lakhkar Khan
Department of Statistics, Government Post Graduate College Mardan, Pakistan
3 shared publications
Latest Publications
- 21 August 2026
- Published by Taylor & Francis in Communications in Statistics - Simulation and Computation
- p. 1-16
Condition number-based new robust ridge M-estimator for linear regression model with penalty of multicollinearity and outlier
1. The multiple linear regression model is: (1) y(nx1)=X(nxp)β(px1)+E (nx1)Here, y (nx1) is the predicted outcome vector, X (nxp) is the data matrix, β (px1) is the coefficients, and E (nx1) is the...
- 21 August 2026
- Published by Taylor & Francis in Communications in Statistics - Simulation and Computation
- p. 1-16
Condition number-based new robust ridge M-estimator for linear regression model with penalty of multicollinearity and outlier
1. The multiple linear regression model is: (1) y(nx1)=X(nxp)β(px1)+E (nx1)Here, y (nx1) is the predicted outcome vector, X (nxp) is the data matrix, β (px1) is the coefficients, and E (nx1) is the...
- 24 July 2026
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 96 (17) , 4647-4664
On some improved two-parameter ridge regression estimators for the heteroscedastic linear regression models: simulation and application
1. Multiple Linear Regression extends simple linear regression by modeling the relationship between multiple independent variables and a single dependent variable. When performing a multiple linear...
- 24 July 2026
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 96 (17) , 4647-4664
On some improved two-parameter ridge regression estimators for the heteroscedastic linear regression models: simulation and application
1. Multiple Linear Regression extends simple linear regression by modeling the relationship between multiple independent variables and a single dependent variable. When performing a multiple linear...
- 7 May 2026
- Published by Taylor & Francis in Statistics
- p. 1-21
Quantile-based two-parameter robust ridge M-estimators for linear regression model for mitigating multicollinearity and outliers
1. Regression analysis (RA) is a fundamental statistical tool, widely applied in economics, social sciences, engineering, and natural sciences. Its main purpose is to describe and model the relatio...
- 7 May 2026
- Published by Taylor & Francis in Statistics
- p. 1-21
Quantile-based two-parameter robust ridge M-estimators for linear regression model for mitigating multicollinearity and outliers
1. Regression analysis (RA) is a fundamental statistical tool, widely applied in economics, social sciences, engineering, and natural sciences. Its main purpose is to describe and model the relatio...
- 22 April 2026
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 55 (17) , 1-27
Bagging-based robust ridge M-estimators in linear regression model for mitigating multicollinearity and outliers
- 22 April 2026
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 55 (17) , 1-27
Bagging-based robust ridge M-estimators in linear regression model for mitigating multicollinearity and outliers
- Open Access
- 13 March 2026
- Published by Taylor & Francis in Research in Statistics
- Vol. 4 (1)
Weighted robust improved KL M-estimators for linear regression model in presence of multicollinearity and outliers: simulation and applications
- 13 March 2026
- Published by Taylor & Francis in Research in Statistics
- Vol. 4 (1)
Open AccessWeighted robust improved KL M-estimators for linear regression model in presence of multicollinearity and outliers: simulation and applications
- 12 March 2026
- Published by Taylor & Francis in Communications in Statistics: Case Studies, Data Analysis and Applications
- Vol. 12 (3) , 1-20
Mitigating multicollinearity and outliers via robust M-based biasing parameter selection in two-parameter ridge regression
1. Regression analysis serves as the fundamental techniques in statistical modeling, enabling researchers to explore and quantify relationships between dependent and independent variables. Among it...
- 12 March 2026
- Published by Taylor & Francis in Communications in Statistics: Case Studies, Data Analysis and Applications
- Vol. 12 (3) , 1-20
Mitigating multicollinearity and outliers via robust M-based biasing parameter selection in two-parameter ridge regression
1. Regression analysis serves as the fundamental techniques in statistical modeling, enabling researchers to explore and quantify relationships between dependent and independent variables. Among it...
- Open Access
- 12 January 2026
- Published by Taylor & Francis in Research in Statistics
- Vol. 4 (1)
Mitigating multicollinearity in ridge regression: a comparative study of quantile-based two parameter weighted KL-estimators
- 12 January 2026
- Published by Taylor & Francis in Research in Statistics
- Vol. 4 (1)
Open AccessMitigating multicollinearity in ridge regression: a comparative study of quantile-based two parameter weighted KL-estimators
- Open Access
- 4 January 2026
- Published by Knowledge Creation and Dissemination Centre in Social Science Review Archives
- Vol. 4 (1) , 4782-4813
Evaluating the Impact of Strategic Time-Outs on Player Performance in T-20 Cricket: A Case Study of the Pakistan Super League
The present study has been conducted to evaluate the effect of strategic time out in t-20 matches. For achieving the goals, a case study of Pakistan super league has been carried out. Data of all matches played in PSL first and second edition except from the matches ended with no result or resulted before the strategic time out for each innings. The concept of latent variables has been taken under consideration in this study. Therefore, structural equation modeling approach has been adopted to check the effect of strategic time out on players’ performance and match result. To fit the model, the latent variables “batting performance” and “bowling performance” splatted in to two parts i.e. before the strategic time point and after the strategic time point and taken as an independent latent variable whereas the dependent latent variable was overall performance i.e. match result. From the analysis it has been observed that the strategic time point is beneficent for the batting performance but not for the bowling performance as the model co-efficient for batting performance were improved while for bowling performance did not. So it is recommended for bowling team that the same strategies should be continue as adopted before the strategic time out. While the batting team should make strategies accordingly.
- 4 January 2026
- Published by Knowledge Creation and Dissemination Centre in Social Science Review Archives
- Vol. 4 (1) , 4782-4813
Open AccessEvaluating the Impact of Strategic Time-Outs on Player Performance in T-20 Cricket: A Case Study of the Pakistan Super League
The present study has been conducted to evaluate the effect of strategic time out in t-20 matches. For achieving the goals, a case study of Pakistan super league has been carried out. Data of all matches played in PSL first and second edition except from the matches ended with no result or resulted before the strategic time out for each innings. The concept of latent variables has been taken under consideration in this study. Therefore, structural equation modeling approach has been adopted to check the effect of strategic time out on players’ performance and match result. To fit the model, the latent variables “batting performance” and “bowling performance” splatted in to two parts i.e. before the strategic time point and after the strategic time point and taken as an independent latent variable whereas the dependent latent variable was overall performance i.e. match result. From the analysis it has been observed that the strategic time point is beneficent for the batting performance but not for the bowling performance as the model co-efficient for batting performance were improved while for bowling performance did not. So it is recommended for bowling team that the same strategies should be continue as adopted before the strategic time out. While the batting team should make strategies accordingly.