An algorithm for estimating survival under a copula-based dependent truncation model

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105711

Title:	An algorithm for estimating survival under a copula-based dependent truncation model
Authors:	江村剛志;Emura, T.;Murotani, K.
Contributors:	理學院統計研究所
Keywords:	Algorithms;Economics;Empirical analysis;Estimating;Estimating techniques;Finance;Illustrations;Insurance;Management;Mathematical analysis;Mathematical models;Mathematics and Statistics;Original Paper;Packages;Statistical Theory and Methods;Statistics;Statistics for Business;Studies;Survival;Survival analysis;Variables
Date:	2015-12-01
Issue Date:	2026-04-23 12:48:43 (UTC+8)
Publisher:	Springer New York;Berlin/Heidelberg: Springer Science and Business Media LLC
Abstract:	摘要： Traditional analysis with truncated survival data has been developed under the assumption that the lifetime variable of interest is statistically independent of the truncation variable. However, empirical evidence has shown that the truncation variable may depend on the lifetime of interest in many real-world examples. The lack of independence can lead to seriously biased analysis. In this article, we revisit an existing estimation procedure for survival under a copula-based dependent truncation model. Here, the same estimating equation is adopted but a different algorithm to solve the equation is proposed. We compare the new algorithm with the existing one and discuss its theoretical and practical usefulness. Real data examples are analyzed for illustration. We implemented the proposed algorithm in an R “depend.truncation” package, available from CRAN. 其他題名： TEST 出版者： Berlin/Heidelberg: Springer Science and Business Media LLC 出版日期： 2015-12-01 出處： TEST, 2015-12, Vol.24 (4), p.734-751 資源來源： SpringerLink Journals - CCNY AutoHoldings 版權： Sociedad de Estadística e Investigación Operativa 2015 識別號： ISSN: 1133-0686 識別號： EISSN: 1863-8260 識別號： DOI: 10.1007/s11749-015-0432-8
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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