Multivariate normal distribution approaches for dependently truncated data

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105855

Title:	Multivariate normal distribution approaches for dependently truncated data
Authors:	江村剛志;Emura, Takeshi;Konno, Yoshihiko
Contributors:	理學院統計研究所
Keywords:	Bootstrap method;Colleges & universities;Computer simulation;Correlation analysis;Economic Theory/Quantitative Economics/Mathematical Methods;Economics;Estimating;Finance;Fittings;Focusing;Insurance;Management;Mathematical analysis;Mathematical models;Mathematics and Statistics;Missing data;Multivariate analysis;Normal distribution;Operations Research/Decision Theory;Probability Theory and Stochastic Processes;Random variables;Regular Article;Statistical methods;Statistics;Statistics for Business;Studies
Date:	2012-01-01
Issue Date:	2026-04-23 12:58:00 (UTC+8)
Publisher:	Springer New York;Berlin/Heidelberg: Springer Science and Business Media LLC
Abstract:	摘要： Many statistical methods for truncated data rely on the independence assumption regarding the truncation variable. In many application studies, however, the dependence between a variable X of interest and its truncation variable L plays a fundamental role in modeling data structure. For truncated data, typical interest is in estimating the marginal distributions of (L, X) and often in examining the degree of the dependence between X and L. To relax the independence assumption, we present a method of fitting a parametric model on (L, X), which can easily incorporate the dependence structure on the truncation mechanisms. Focusing on a specific example for the bivariate normal distribution, the score equations and Fisher information matrix are provided. A robust procedure based on the bivariate t-distribution is also considered. Simulations are performed to examine finite-sample performances of the proposed method. Extension of the proposed method to doubly truncated data is briefly discussed. 其他題名： Stat Papers 出版者： Berlin/Heidelberg: Springer Science and Business Media LLC 出版日期： 2012-02-01 出處： Statistical Papers, 2012-02, Vol.53 (1), p.133-149 資源來源： EBSCOhost Business Source Premier 版權： Springer-Verlag 2010 版權： Springer-Verlag 2012 識別號： ISSN: 0932-5026 識別號： EISSN: 1613-9798 識別號： DOI: 10.1007/s00362-010-0321-x
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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