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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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Detalles Bibliográficos
Autores principales: Teng, Guangqiang, Tian, Boping, Zhang, Yuanyuan, Fu, Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
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
Descripción
Sumario: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.