Please use this identifier to cite or link to this item:
Title: Empirical likelihood confidence regions for one- or two- samples with doubly censored data
Authors: Shen, J
Yuen, KC
Liu, C 
Issue Date: Jan-2016
Source: Computational statistics and data analysis, Jan. 2016, v. 93, p. 285-293
Abstract: The purpose is to propose a new EM algorithm for doubly censored data subject to nonparametric moment constraints. Empirical likelihood confidence regions are constructed for one- or two- samples of doubly censored data. It is shown that the corresponding empirical likelihood ratio converges to a standard chi-square random variable. Simulations are carried out to assess the finite-sample performance of the proposed method. For illustration purpose, the proposed method is applied to a real data set with two samples.
Keywords: Chi-square convergence
Confidence region
Doubly censored data
EM algorithm
Empirical likelihood ratio
Moment constraint
Publisher: Elsevier
Journal: Computational Statistics and Data Analysis 
ISSN: 0167-9473
DOI: 10.1016/j.csda.2015.01.010
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Aug 29, 2020

Page view(s)

Last Week
Last month
Citations as of Sep 27, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.