Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design

被引：1

作者：

Teng, Guangqiang ^{[1
]}

Tian, Boping ^{[1
]}

Zhang, Yuanyuan ^{[2
]}

Fu, Sheng ^{[3
]}

机构：

[1] Harbin Inst Technol, Sch Math, Harbin 150001, Peoples R China

[2] Soochow Univ, Sch Math Sci, Suzhou 215006, Peoples R China

[3] Natl Univ Singapore, Dept Ind Syst Engn & Management, 21 Lowr Kent Ridge Rd, Singapore 119077, Singapore

来源：

ENTROPY | 2023年 / 25卷 / 01期

基金：

中国国家自然科学基金;

关键词：

generalized linear models; massive data; nonnatural links; unbounded covariates; unconditional subsampling estimator; NORMALITY; CONSISTENCY;

D O I：

10.3390/e25010084

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页数：21

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