DOAJ Open Access 2022

Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design

Guangqiang Teng Boping Tian Yuanyuan Zhang Sheng Fu

Abstrak

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.

Topik & Kata Kunci

Science Astrophysics Physics

Penulis (4)

Guangqiang Teng

Boping Tian

Yuanyuan Zhang

Sheng Fu

Format Sitasi

APA MLA BibTeX

Teng, G., Tian, B., Zhang, Y., Fu, S. (2022). Asymptotics of Subsampling for Generalized Linear Regression Models under Unbounded Design. https://doi.org/10.3390/e25010084

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Informasi Jurnal

Tahun Terbit: 2022
Sumber Database: DOAJ
DOI: 10.3390/e25010084
Akses: Open Access ✓