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. © 2021 Taylor & Francis Group, LLC.