Document Type

Thesis

Degree

Master of Science (MS)

Major/Program

Statistics

First Advisor's Name

B.M. Golam Kibria

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Jie Mi

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Florence George

Third Advisor's Committee Title

Committee Member

Keywords

ridge regression estimators, logistic regression, multicollinearity

Date of Defense

3-28-2018

Abstract

The purpose of this research is to investigate the performance of some ridge regression estimators for the logistic regression model in the presence of moderate to high correlation among the explanatory variables. As a performance criterion, we use the mean square error (MSE), the mean absolute percentage error (MAPE), the magnitude of bias, and the percentage of times the ridge regression estimator produces a higher MSE than the maximum likelihood estimator. A Monto Carlo simulation study has been executed to compare the performance of the ridge regression estimators under different experimental conditions. The degree of correlation, sample size, number of independent variables, and log odds ratio has been varied in the design of experiment. Simulation results show that under certain conditions, the ridge regression estimators outperform the maximum likelihood estimator. Moreover, an empirical data analysis supports the main findings of this study. This thesis proposed and recommended some good ridge regression estimators of the logistic regression model for the practitioners in the field of health, physical and social sciences.

Identifier

FIDC006547

Available for download on Monday, October 22, 2018

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