Bootstrap tests for overdispersion in a zero-inflated poisson regression model

Byoung Cheol Jung; Myoungshic Jhun; Jae Won Lee

doi:10.1111/j.1541-0420.2005.00368.x

Bootstrap tests for overdispersion in a zero-inflated poisson regression model

Byoung Cheol Jung, Myoungshic Jhun, Jae Won Lee

Department of Statistics

Korea University

Research output: Contribution to journal › Review article › peer-review

18 Scopus citations

Abstract

Ridout, Hinde, and Demétrio (2001, Biometrics 57, 219-223) derived a score test for testing a zero-inflated Poisson (ZIP) regression model against zero-inflated negative binomial (ZINB) alternatives. They mentioned that the score test using the normal approximation might underestimate the nominal significance level possibly for small sample cases. To remedy this problem, a parametric hootstrap method is proposed. It is shown that the bootstrap method keeps the significance level close to the nominal one and has greater power uniformly than the existing normal approximation for testing the hypothesis.

Original language	English
Pages (from-to)	626-628
Number of pages	3
Journal	Biometrics
Volume	61
Issue number	2
DOIs	https://doi.org/10.1111/j.1541-0420.2005.00368.x
State	Published - Jun 2005

Keywords

Bootstrap
Count data
Negative binomial
Poisson regression model
Score test
Zero inflation

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title = "Bootstrap tests for overdispersion in a zero-inflated poisson regression model",

abstract = "Ridout, Hinde, and Dem{\'e}trio (2001, Biometrics 57, 219-223) derived a score test for testing a zero-inflated Poisson (ZIP) regression model against zero-inflated negative binomial (ZINB) alternatives. They mentioned that the score test using the normal approximation might underestimate the nominal significance level possibly for small sample cases. To remedy this problem, a parametric hootstrap method is proposed. It is shown that the bootstrap method keeps the significance level close to the nominal one and has greater power uniformly than the existing normal approximation for testing the hypothesis.",

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AU - Jhun, Myoungshic

AU - Lee, Jae Won

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N2 - Ridout, Hinde, and Demétrio (2001, Biometrics 57, 219-223) derived a score test for testing a zero-inflated Poisson (ZIP) regression model against zero-inflated negative binomial (ZINB) alternatives. They mentioned that the score test using the normal approximation might underestimate the nominal significance level possibly for small sample cases. To remedy this problem, a parametric hootstrap method is proposed. It is shown that the bootstrap method keeps the significance level close to the nominal one and has greater power uniformly than the existing normal approximation for testing the hypothesis.

AB - Ridout, Hinde, and Demétrio (2001, Biometrics 57, 219-223) derived a score test for testing a zero-inflated Poisson (ZIP) regression model against zero-inflated negative binomial (ZINB) alternatives. They mentioned that the score test using the normal approximation might underestimate the nominal significance level possibly for small sample cases. To remedy this problem, a parametric hootstrap method is proposed. It is shown that the bootstrap method keeps the significance level close to the nominal one and has greater power uniformly than the existing normal approximation for testing the hypothesis.

KW - Bootstrap

KW - Count data

KW - Negative binomial

KW - Poisson regression model

KW - Score test

KW - Zero inflation

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ER -

Bootstrap tests for overdispersion in a zero-inflated poisson regression model

Abstract

Keywords

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