Biased proportional hazard regression estimator in the existence of collinearity

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, las...

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Main Authors: Anu Sirohi, Basim S.O. Alsaedi, Marwan H. Ahelali, Mahesh Kumar Jayaswal
Format: Article
Language:English
Published: Elsevier 2023-11-01
Series:Heliyon
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844023086024
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author Anu Sirohi
Basim S.O. Alsaedi
Marwan H. Ahelali
Mahesh Kumar Jayaswal
author_facet Anu Sirohi
Basim S.O. Alsaedi
Marwan H. Ahelali
Mahesh Kumar Jayaswal
author_sort Anu Sirohi
collection DOAJ
description 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.
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spelling doaj.art-668dd775460548a687e2411930ffed032023-12-02T07:02:06ZengElsevierHeliyon2405-84402023-11-01911e21394Biased proportional hazard regression estimator in the existence of collinearityAnu Sirohi0Basim S.O. Alsaedi1Marwan H. Ahelali2Mahesh Kumar Jayaswal3Department of Statistics, AIAS, Amity University, Noida, India; Corresponding author.Department of Statistics, University of Tabuk, Tabuk 71491, Saudi ArabiaDepartment of Statistics, University of Tabuk, Tabuk 71491, Saudi ArabiaDepartment of Mathematics and Statistics, Banasthali Vidyapith, Rajasthan, IndiaThis 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.http://www.sciencedirect.com/science/article/pii/S2405844023086024CollinearityElastic netInfant mortalityPrincipal component regressionProportional hazard regression model
spellingShingle Anu Sirohi
Basim S.O. Alsaedi
Marwan H. Ahelali
Mahesh Kumar Jayaswal
Biased proportional hazard regression estimator in the existence of collinearity
Heliyon
Collinearity
Elastic net
Infant mortality
Principal component regression
Proportional hazard regression model
title Biased proportional hazard regression estimator in the existence of collinearity
title_full Biased proportional hazard regression estimator in the existence of collinearity
title_fullStr Biased proportional hazard regression estimator in the existence of collinearity
title_full_unstemmed Biased proportional hazard regression estimator in the existence of collinearity
title_short Biased proportional hazard regression estimator in the existence of collinearity
title_sort biased proportional hazard regression estimator in the existence of collinearity
topic Collinearity
Elastic net
Infant mortality
Principal component regression
Proportional hazard regression model
url http://www.sciencedirect.com/science/article/pii/S2405844023086024
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