Related Experiment Video
Updated: Jul 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Biased proportional hazard regression estimator in the existence of collinearity
Anu Sirohi1, Basim S O Alsaedi2, Marwan H Ahelali2
1Department of Statistics, AIAS, Amity University, Noida, 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, 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.
More Related Videos
Related Concept Videos
Hazard Rate
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Hazard Ratio
For example, in a clinical trial...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Coefficient of Correlation
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
Friedman Two-way Analysis of Variance by Ranks

