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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Detalles Bibliográficos
Autores principales: Sirohi, Anu, Alsaedi, Basim S.O., Ahelali, Marwan H., Jayaswal, Mahesh Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665663/
https://www.ncbi.nlm.nih.gov/pubmed/38027716
http://dx.doi.org/10.1016/j.heliyon.2023.e21394
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author Sirohi, Anu
Alsaedi, Basim S.O.
Ahelali, Marwan H.
Jayaswal, Mahesh Kumar
author_facet Sirohi, Anu
Alsaedi, Basim S.O.
Ahelali, Marwan H.
Jayaswal, Mahesh Kumar
author_sort Sirohi, Anu
collection PubMed
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, [Formula: see text] 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 pubmed-106656632023-10-29 Biased proportional hazard regression estimator in the existence of collinearity Sirohi, Anu Alsaedi, Basim S.O. Ahelali, Marwan H. Jayaswal, Mahesh Kumar Heliyon Research Article 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, [Formula: see text] 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. Elsevier 2023-10-29 /pmc/articles/PMC10665663/ /pubmed/38027716 http://dx.doi.org/10.1016/j.heliyon.2023.e21394 Text en © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Sirohi, Anu
Alsaedi, Basim S.O.
Ahelali, Marwan H.
Jayaswal, Mahesh Kumar
Biased proportional hazard regression estimator in the existence of collinearity
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 Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665663/
https://www.ncbi.nlm.nih.gov/pubmed/38027716
http://dx.doi.org/10.1016/j.heliyon.2023.e21394
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