Biased proportional hazard regression estimator in the existence of collinearity

Anu Sirohi; Basim S O Alsaedi; Marwan H Ahelali; Mahesh Kumar Jayaswal

doi:10.1016/j.heliyon.2023.e21394

Biased proportional hazard regression estimator in the existence of collinearity

Heliyon. 2023 Oct 29;9(11):e21394. doi: 10.1016/j.heliyon.2023.e21394. eCollection 2023 Nov.

Authors

Anu Sirohi¹, Basim S O Alsaedi², Marwan H Ahelali², Mahesh Kumar Jayaswal³

Affiliations

¹ Department of Statistics, AIAS, Amity University, Noida, India.
² Department of Statistics, University of Tabuk, Tabuk 71491, Saudi Arabia.
³ Department of Mathematics and Statistics, Banasthali Vidyapith, Rajasthan, India.

Abstract

This paper proposed a new biased proportional hazard regression (PHR) estimator which is the combination of elastic net proportional hazard regression (ENPHR) and principal components proportional hazard regression (PCPHR) estimator. Comparison of proposed estimator with ENPHR, PCPHR, ridge PHR, lasso PHR, $r - k$ class PHR and maximum likelihood (ML) estimators is done in terms of scalar mean square error (MSE). Simulation study is conducted to examine the performance of each estimator. Furthermore, the developed estimator is utilized to analyze the infant mortality in Delhi, India.

Keywords: Collinearity; Elastic net; Infant mortality; Principal component regression; Proportional hazard regression model.