Parametric Frailty Analysis in Presence of Collinearity: An Application to Assessment of Infant Mortality

Olayan Albalawi, Anu Sirohi, Piyush Kant Rai and Ayed R. A. Alanzi
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Olayan Albalawi: Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 47512, Saudi Arabia
Anu Sirohi: Department of Mathematics, Sharda University, Greater Noida 201310, India
Piyush Kant Rai: Department of Statistics, Institute of Science, Banaras Hindu University, Varanasi 221005, India
Ayed R. A. Alanzi: Department of Mathematics, College of Science and Arts in Gurayat, Jouf University, Gurayat 77454, Saudi Arabia

Mathematics, 2022, vol. 10, issue 13, 1-10

Abstract: This paper analyzes the time to event data in the presence of collinearity. To address collinearity, the ridge regression estimator was applied in multiple and logistic regression as an alternative to the maximum likelihood estimator (MLE), among others. It has a smaller mean square error (MSE) and is therefore more precise. This paper generalizes the approach to address collinearity in the frailty model, which is a random effect model for the time variable. A simulation study is conducted to evaluate its performance. Furthermore, the proposed method is applied on real life data taken from the largest sample survey of India, i.e., national family health survey (2005–2006 ) data to evaluate the association of different determinants on infant mortality in India.

Keywords: frailty model; collinearity; maximum likelihood estimation; ridge regression; infant mortality (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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