Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design

The optimal subsampling is an statistical methodology for generalized linear models (GLMs) to make inference quickly about parameter estimation in massive data regression. Existing literature only considers bounded covariates. In this paper, the asymptotic normality of the subsampling M-estimator ba...

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Autores principales: Teng, Guangqiang, Tian, Boping, Zhang, Yuanyuan, Fu, Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858070/
https://www.ncbi.nlm.nih.gov/pubmed/36673225
http://dx.doi.org/10.3390/e25010084
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author Teng, Guangqiang
Tian, Boping
Zhang, Yuanyuan
Fu, Sheng
author_facet Teng, Guangqiang
Tian, Boping
Zhang, Yuanyuan
Fu, Sheng
author_sort Teng, Guangqiang
collection PubMed
description The optimal subsampling is an statistical methodology for generalized linear models (GLMs) to make inference quickly about parameter estimation in massive data regression. Existing literature only considers bounded covariates. In this paper, the asymptotic normality of the subsampling M-estimator based on the Fisher information matrix is obtained. Then, we study the asymptotic properties of subsampling estimators of unbounded GLMs with nonnatural links, including conditional asymptotic properties and unconditional asymptotic properties.
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spelling pubmed-98580702023-01-21 Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design Teng, Guangqiang Tian, Boping Zhang, Yuanyuan Fu, Sheng Entropy (Basel) Article The optimal subsampling is an statistical methodology for generalized linear models (GLMs) to make inference quickly about parameter estimation in massive data regression. Existing literature only considers bounded covariates. In this paper, the asymptotic normality of the subsampling M-estimator based on the Fisher information matrix is obtained. Then, we study the asymptotic properties of subsampling estimators of unbounded GLMs with nonnatural links, including conditional asymptotic properties and unconditional asymptotic properties. MDPI 2022-12-31 /pmc/articles/PMC9858070/ /pubmed/36673225 http://dx.doi.org/10.3390/e25010084 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Teng, Guangqiang
Tian, Boping
Zhang, Yuanyuan
Fu, Sheng
Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design
title Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design
title_full Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design
title_fullStr Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design
title_full_unstemmed Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design
title_short Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design
title_sort asymptotics of subsampling for generalized linear regression models under unbounded design
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858070/
https://www.ncbi.nlm.nih.gov/pubmed/36673225
http://dx.doi.org/10.3390/e25010084
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AT zhangyuanyuan asymptoticsofsubsamplingforgeneralizedlinearregressionmodelsunderunboundeddesign
AT fusheng asymptoticsofsubsamplingforgeneralizedlinearregressionmodelsunderunboundeddesign

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