Weighted Empirical Likelihood for Accelerated Life Model with Various Types of Censored Data

Jian-Jian Ren () and Yiming Lyu
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Jian-Jian Ren: Statistics Program, Department of Mathematics, University of Maryland, College Park, MD 20742, USA
Yiming Lyu: The Janssen Pharmaceutical Company of Johnson & Johnson, New Brunswick, NJ 08933, USA

Stats, 2024, vol. 7, issue 3, 1-11

Abstract: In analysis of survival data, the Accelerated Life Model (ALM) is one of the widely used semiparametric models, and we often encounter various types of censored survival data, such as right censored data, doubly censored data, interval censored data, partly interval-censored data, etc. For complicated types of censored data, the studies of statistical inferences on the ALM are very technical and challenging mathematically, thus up to now little work has been done. In this article, we extend the concept of weighted empirical likelihood (WEL) from univariate case to multivariate case, and we apply it to the ALM, which leads to an estimation approach, called weighted maximum likelihood estimator , as well as the WEL based confidence interval for the regression parameter. Our proposed procedures are applicable to various types of censored data under a unified framework, and some simulation results are presented.

Keywords: doubly censored data; empirical likelihood; interval censored data; maximum likelihood estimator; partly interval-censored data; right censored data; weighted empirical likelihood (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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