Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105902

Title:	Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation
Authors:	江村剛志;Emura, Takeshi;Konno, Yoshihiko;Michimae, Hirofumi
Contributors:	理學院統計研究所
Keywords:	Age;Algorithms;Asymptotic methods;Biostatistics;Bootstrap method;Cancer;Child;Childhood;Computer Simulation;Economics;Finance;Health Sciences;Humans;Insurance;Lifetime;Likelihood Functions;Management;Mathematics and Statistics;Medical diagnosis;Medicine;Neoplasms;Neoplasms - epidemiology;Operations Research/Decision Theory;Quality Control;Random variables;Reliability;Safety and Risk;Statistical inference;Statistics;Statistics for Business;Statistics for Life Sciences;Statistics, Nonparametric;Studies;Survival Analysis
Date:	2015-07-15
Issue Date:	2026-04-23 13:00:28 (UTC+8)
Publisher:	Springer Netherlands;New York: Springer Science and Business Media LLC
Abstract:	摘要： Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) that is obtained through a self-consistency algorithm. Owing to the complicated asymptotic distribution of the NPMLE, the bootstrap method has been suggested for statistical inference. This paper proposes a closed-form estimator for the asymptotic covariance function of the NPMLE, which is computationally attractive alternative to bootstrapping. Furthermore, we develop various statistical inference procedures, such as confidence interval, goodness-of-fit tests, and confidence bands to demonstrate the usefulness of the proposed covariance estimator. Simulations are performed to compare the proposed method with both the bootstrap and jackknife methods. The methods are illustrated using the childhood cancer dataset. 其他題名： Lifetime Data Anal 出版者： New York: Springer Science and Business Media LLC 出版日期： 2015-07-01 出處： Lifetime Data Analysis, 2015-07, Vol.21 (3), p.397-418 資源來源： Healthcare Administration Database (Proquest) 版權： Springer Science+Business Media New York 2014 版權： Springer Science+Business Media New York 2015 識別號： ISSN: 1380-7870 識別號： ISSN: 1572-9249 識別號： EISSN: 1572-9249 識別號： DOI: 10.1007/s10985-014-9297-5 識別號： PMID: 25001399
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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