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
Descripción
Sumario: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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