Maximum likelihood estimation for a special exponential family under random double-truncation

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105826

Title:	Maximum likelihood estimation for a special exponential family under random double-truncation
Authors:	江村剛志;Hu, Ya-Hsuan;Emura, Takeshi
Contributors:	理學院統計研究所
Keywords:	Algorithms;Computation;Data processing;Density;Economic Theory/Quantitative Economics/Mathematical Methods;Mathematics and Statistics;Nonparametric statistics;Normal distribution;Original Paper;Probability and Statistics in Computer Science;Probability Theory and Stochastic Processes;Samples;Statistical analysis;Statistical methods;Statistics;Survival analysis
Date:	2015-12-01
Issue Date:	2026-04-23 12:55:20 (UTC+8)
Publisher:	Springer Verlag;Berlin/Heidelberg: Springer Berlin Heidelberg
Abstract:	摘要： Doubly-truncated data often appear in lifetime data analysis, where samples are collected under certain time constraints. Nonparametric methods for doubly-truncated data have been studied well in the literature. Alternatively, this paper considers parametric inference when samples are subject to double-truncation. Efron and Petrosian (J Am Stat Assoc 94:824–834, 1999 ) proposed to fit a parametric family, called the special exponential family, with doubly-truncated data. However, non-trivial technical aspects, such as parameter space, support of the density, and computational algorithms, have not been discussed in the literature. This paper fills this gap by providing the technical aspects, including adequate choices of parameter space as well as support, and reliable computational algorithms. Simulations are conducted to verify the suggested techniques, and real data are used for illustration. 其他題名： Comput Stat 出版者： Berlin/Heidelberg: Springer Berlin Heidelberg 出版日期： 2015-12-01 出處： Computational statistics, 2015-12, Vol.30 (4), p.1199-1229 資源來源： SpringerLink Journals 版權： Springer-Verlag Berlin Heidelberg 2015 識別號： ISSN: 0943-4062 識別號： EISSN: 1613-9658 識別號： DOI: 10.1007/s00180-015-0564-z
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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