A jackknife-based versatile test for two-sample problems with right-censored data

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105681

Title:	A jackknife-based versatile test for two-sample problems with right-censored data
Authors:	陳春樹;Chang, Yu-Mei;Chen, Chun-Shu;Shen, Pao-Sheng
Contributors:	理學院統計研究所
Keywords:	Applied statistics;data driven;Defects;Estimating techniques;linear combination test;Mathematical analysis;Mathematical models;Parameter estimation;right-censored data;Statistical analysis;Studies;Survival;Variance;weighted Kaplan-Meier test;weighted logrank test
Date:	2012-02-01
Issue Date:	2026-04-23 12:47:17 (UTC+8)
Publisher:	Routledge;Abingdon: Taylor & Francis
Abstract:	摘要： For testing the equality of two survival functions, the weighted logrank test and the weighted Kaplan-Meier test are the two most widely used methods. Actually, each of these tests has advantages and defects against various alternatives, while we cannot specify in advance the possible types of the survival differences. Hence, how to choose a single test or combine a number of competitive tests for indicating the diversities of two survival functions without suffering a substantial loss in power is an important issue. Instead of directly using a particular test which generally performs well in some situations and poorly in others, we further consider a class of tests indexed by a weighted parameter for testing the equality of two survival functions in this paper. A delete-1 jackknife method is implemented for selecting weights such that the variance of the test is minimized. Some numerical experiments are performed under various alternatives for illustrating the superiority of the proposed method. Finally, the proposed testing procedure is applied to two real-data examples as well. 出版者： Abingdon: Taylor & Francis 出版日期： 2012-02 出處： Journal of applied statistics, 2012-02, Vol.39 (2), p.267-277 資源來源： Taylor & Francis Journals Auto-Holdings Collection 版權： Copyright Taylor & Francis Group, LLC 2012 版權： Copyright Taylor & Francis Ltd. 2012 識別號： ISSN: 0266-4763 識別號： EISSN: 1360-0532 識別號： DOI: 10.1080/02664763.2011.584524
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	18	View/Open

社群 sharing

Loading...