JOURNAL ARTICLE

Jackknife empirical likelihood test for mean residual life functions

Ying‐Ju ChenWei NingArjun K. Gupta

Year: 2016 Journal:   Communication in Statistics- Theory and Methods Vol: 46 (7)Pages: 3111-3122   Publisher: Taylor & Francis

Abstract

Mean residual life (MRL) function is an important function in survival analysis which describes the expected remaining life given survival to a certain age. In this article, we propose a non parametric method based on jackknife empirical likelihood through a U-statistic to test the equality of two mean residual functions. The asymptotic distribution of the test statistic has been derived. Simulations are conducted to illustrate the performance of the proposed test under different distributional assumptions and compare with some existing method. The proposed method is then applied to two real datasets.

Keywords:
Jackknife resampling Residual Statistics Mathematics Empirical likelihood Statistic Parametric statistics Test statistic Survival function Econometrics Statistical hypothesis testing Survival analysis Algorithm

Metrics

4
Cited By
0.35
FWCI (Field Weighted Citation Impact)
40
Refs
0.70
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Statistical Distribution Estimation and Applications
Physical Sciences →  Mathematics →  Statistics and Probability
Statistical Methods and Inference
Physical Sciences →  Mathematics →  Statistics and Probability
Statistical Methods and Bayesian Inference
Physical Sciences →  Mathematics →  Statistics and Probability

Related Documents

JOURNAL ARTICLE

Jackknife Empirical Likelihood Ratio Test for Decreasing Mean Residual Life

Sreelakshmi, N.

Journal:   OPAL (Open@LaTrobe) (La Trobe University) Year: 2025
JOURNAL ARTICLE

Jackknife Empirical Likelihood Ratio Test for Decreasing Mean Residual Life

N. Sreelakshmi

Journal:   American Journal of Mathematical and Management Sciences Year: 2025 Vol: 44 (2)Pages: 178-189
JOURNAL ARTICLE

A consistent jackknife empirical likelihood test for distribution functions

Xiaohui LiuQihua WangYi Liu

Journal:   Annals of the Institute of Statistical Mathematics Year: 2016 Vol: 69 (2)Pages: 249-269
© 2026 ScienceGate Book Chapters — All rights reserved.