Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data

Empirical likelihood as a nonparametric approach has been demonstrated to have many desirable merits for constructing a confidence region. The purpose of this article is to apply the empirical likelihood method to study the generalized functional-coefficient regression models with multiple smoothing...

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Main Authors: Hong-Xia Xu, Han-Sheng Zhong, Guo-Liang Fan
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2020/1261426
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author Hong-Xia Xu
Han-Sheng Zhong
Guo-Liang Fan
author_facet Hong-Xia Xu
Han-Sheng Zhong
Guo-Liang Fan
author_sort Hong-Xia Xu
collection DOAJ
description Empirical likelihood as a nonparametric approach has been demonstrated to have many desirable merits for constructing a confidence region. The purpose of this article is to apply the empirical likelihood method to study the generalized functional-coefficient regression models with multiple smoothing variables when the response is subject to random right censoring. The coefficient functions with multiple smoothing variables can accommodate various nonlinear interaction effects between covariates. The empirical log-likelihood ratio of an unknown parameter is constructed and shown to have a standard chi-squared limiting distribution at the true parameter. Based on this, the confidence region of the unknown parameter can be constructed. Simulation studies are carried out to indicate that the empirical likelihood method performs better than a normal approximation-based approach for constructing the confidence region.
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publishDate 2020-01-01
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spelling doaj-art-9050db90026d487a98f59d93e1d3b2512025-08-20T02:09:14ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/12614261261426Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring DataHong-Xia Xu0Han-Sheng Zhong1Guo-Liang Fan2Department of Mathematics, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Economics and Management, Shanghai Maritime University, Shanghai 201306, ChinaSchool of Economics and Management, Shanghai Maritime University, Shanghai 201306, ChinaEmpirical likelihood as a nonparametric approach has been demonstrated to have many desirable merits for constructing a confidence region. The purpose of this article is to apply the empirical likelihood method to study the generalized functional-coefficient regression models with multiple smoothing variables when the response is subject to random right censoring. The coefficient functions with multiple smoothing variables can accommodate various nonlinear interaction effects between covariates. The empirical log-likelihood ratio of an unknown parameter is constructed and shown to have a standard chi-squared limiting distribution at the true parameter. Based on this, the confidence region of the unknown parameter can be constructed. Simulation studies are carried out to indicate that the empirical likelihood method performs better than a normal approximation-based approach for constructing the confidence region.http://dx.doi.org/10.1155/2020/1261426
spellingShingle Hong-Xia Xu
Han-Sheng Zhong
Guo-Liang Fan
Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data
Discrete Dynamics in Nature and Society
title Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data
title_full Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data
title_fullStr Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data
title_full_unstemmed Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data
title_short Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data
title_sort empirical likelihood for generalized functional coefficient regression models with multiple smoothing variables under right censoring data
url http://dx.doi.org/10.1155/2020/1261426
work_keys_str_mv AT hongxiaxu empiricallikelihoodforgeneralizedfunctionalcoefficientregressionmodelswithmultiplesmoothingvariablesunderrightcensoringdata
AT hanshengzhong empiricallikelihoodforgeneralizedfunctionalcoefficientregressionmodelswithmultiplesmoothingvariablesunderrightcensoringdata
AT guoliangfan empiricallikelihoodforgeneralizedfunctionalcoefficientregressionmodelswithmultiplesmoothingvariablesunderrightcensoringdata