Bootstrap Tests for Overdispersion in a Zero-Inflated Poisson Regression Model

Bootstrap Tests for Overdispersion in a Zero-Inflated Poisson Regression Model

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 si...

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Veröffentlicht in:Biometrics 2005-06, Vol.61 (2), p.626-628
Hauptverfasser: Jung, Byoung Cheol, Jhun, Myoungshic, Lee, Jae Won
Format: Artikel
Sprache:eng
Schlagworte:
Approximation
Binomials
Biometrics
Biometry - methods
Bootstrap
Bootstrap method
Bootstrap resampling
Computer Simulation
Count data
Models, Statistical
Monte Carlo Method
Negative binomial
P values
Plant Roots - growth & development
Poisson Distribution
Poisson regression model
Random numbers
Reader Reaction
Regression Analysis
Score test
Significance level
Statistical methods
Zero inflation
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Beschreibung
Zusammenfassung: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 bootstrap 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.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.1541-0420.2005.00368.x