Bootstrapping generalized linear models

Lawrence H. Moulton; Scott L. Zeger

doi:10.1016/0167-9473(91)90052-4

Bootstrapping generalized linear models

Lawrence H. Moulton, Scott L. Zeger

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

Several methods for bootstrapping generalized linear regression models are introduced. One-step techniques, both conditional and unconditional on the covariates, are examined with respect to robustness and coverage properties.

Original language	English (US)
Pages (from-to)	53-63
Number of pages	11
Journal	Computational Statistics and Data Analysis
Volume	11
Issue number	1
DOIs	https://doi.org/10.1016/0167-9473(91)90052-4
State	Published - Jan 1991

Keywords

Bootstrap
Generalized linear models
Logistic regression
Overdispersion
Robustness

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/0167-9473(91)90052-4

Cite this

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title = "Bootstrapping generalized linear models",

abstract = "Several methods for bootstrapping generalized linear regression models are introduced. One-step techniques, both conditional and unconditional on the covariates, are examined with respect to robustness and coverage properties.",

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year = "1991",

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KW - Logistic regression

KW - Overdispersion

KW - Robustness

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Bootstrapping generalized linear models

Abstract

Keywords

ASJC Scopus subject areas

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