Some r – k class proportional hazard regression models in the presence of collinearity: an evidence from Indian infant mortality

Abstract

Proportional hazard regression (PHR) model is used to analyze the time to event data in terms of a set of explanatory variables. The estimation and interpretation of the model parameters are unstable, when there is collinearity between explanatory variables. In order to improve the estimation of proportional hazard model with continuous covariates, the r-k class proportional hazard estimator is proposed, which combines the ridge proportional hazard regression (ridge PHR) and principal component proportional hazard regression (PCPHR). The comparisons of the r-k class PHR, ridge PHR, and PCPHR estimators to the maximum likelihood (ML) according to the asymptotic scalar mean square error (MSE) criterion are done. Simulation study is done to evaluate its performance. Furthermore, the proposed method is applied to assess the infant mortality in India.

Keywords:

• Ridge regression
• Principal component regression
• Collinearity
• Proportional hazard model
• Infant mortality

MATHEMATICAL SUBJECT CLASSIFICATION:

• 62N0

Acknowledgments

Authors are very grateful to editor in chief and learned referee for their constructive and novel suggestions to improve the quality appearence of the present article.