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

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 complicat...

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Bibliographic Details
Published in:Stats (Basel, Switzerland) Switzerland), 2024-09, Vol.7 (3), p.944-954
Main Authors: Ren, Jian-Jian, Lyu, Yiming
Format: Article
Language:English
Subjects:
Accelerated life testing
Breast cancer
doubly censored data
Electronic data processing
empirical likelihood
interval censored data
Maximum likelihood estimates (Statistics)
maximum likelihood estimator
Methods
partly interval-censored data
Random variables
Regression analysis
right censored data
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Summary: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.
ISSN:2571-905X
2571-905X
DOI:10.3390/stats7030057