Efficient and improved ridge-type shrinkage estimators in low and high dimensional cox proportional hazards regression model

Seyedeh Zahra Aghamohammadi ()
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Seyedeh Zahra Aghamohammadi: Islamic Azad University

Sankhya B: The Indian Journal of Statistics, 2025, vol. 87, issue 2, No 2, 400-433

Abstract: Abstract In this paper, we consider ridge-type shrinkage estimators to estimate the coefficients in the low and high-dimensional Cox proportional hazards regression model in the presence of multicollinearity. The proposed estimators are evaluated analytically using their asymptotic bias and risk. Moreover, we consider two penalized estimators, namely, the LASSO and Elastic-Net and compare their relative performance with the other proposed estimators numerically. Monte-Carlo simulation experiment and real data analysis are conducted to evaluate the performance of each estimator based on the simulated relative efficiency. The results show that both in low and high-dimensional data, new shrinkage estimators dominate the usual ridge estimator that discards weak predictors. We also find that developed methods would be useful for the practitioners in various sciences.

Keywords: Ridge-type shrinkage; asymptotic bias and risk; High-dimensional regression; Penalized estimator; Relative efficiency; Cox proportional hazards model; Primary 62N01; 62J07; Secondary 62F12; 62P10 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13571-025-00363-1

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