Nonparametric estimations of quantile residual life function with censored length-biased data

IF 2.1 2区 数学 Q1 MATHEMATICS, APPLIED Journal of Computational and Applied Mathematics Pub Date : 2025-03-01 DOI:10.1016/j.cam.2025.116606
Hongping Wu , Ang Shan , Xiaosha Li
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Abstract

In biomedical studies, the median or quantile residual life is often treated as an important quantitative measure for the length of individuals’ residual life besides the mean residual life. In this paper, two nonparametric estimating methods for quantile residual life function are developed with censored length-biased data, and they are constructed based on the moment-based estimation idea and martingale theory, respectively. In particular, the proposed martingale-based estimating method can avoid estimating the survival function of the target population or the right-censoring variable. The consistency and weak convergence of two estimations are also established. In order to evaluate their performance and accuracy in a finite sample, a series of small simulation studies are carried out, too. Finally, an analysis of the famous Channing House data is provided.
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CiteScore
5.40
自引率
4.20%
发文量
437
审稿时长
3.0 months
期刊介绍: The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
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