A score test for zero-inflation in correlated count data

Liming Xiang; Andy H. Lee; Kelvin K.W. Yau; Geoffrey J. McLachlan

doi:10.1002/sim.2308

A score test for zero-inflation in correlated count data

Liming Xiang, Andy H. Lee, Kelvin K.W. Yau, Geoffrey J. McLachlan

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

33 Citations (Scopus)

Abstract

To account for the preponderance of zero counts and simultaneous correlation of observations, a class of zero-inflated Poisson mixed regression models is applicable for accommodating the within-cluster dependence. In this paper, a score test for zero-inflation is developed for assessing correlated count data with excess zeros. The sampling distribution and the power of the test statistic are evaluated by simulation studies. The results show that the test statistic performs satisfactorily under a wide range of conditions. The test procedure is further illustrated using a data set on recurrent urinary tract infections. Copyright © 2005 John Wiley & Sons, Ltd.

Original language	English
Pages (from-to)	1660-1671
Journal	Statistics in Medicine
Volume	25
Issue number	10
DOIs	https://doi.org/10.1002/sim.2308
Publication status	Published - 30 May 2006

Research Keywords

Count data
Poisson mixed model
Power
Score test
Zero-inflation

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title = "A score test for zero-inflation in correlated count data",

abstract = "To account for the preponderance of zero counts and simultaneous correlation of observations, a class of zero-inflated Poisson mixed regression models is applicable for accommodating the within-cluster dependence. In this paper, a score test for zero-inflation is developed for assessing correlated count data with excess zeros. The sampling distribution and the power of the test statistic are evaluated by simulation studies. The results show that the test statistic performs satisfactorily under a wide range of conditions. The test procedure is further illustrated using a data set on recurrent urinary tract infections. Copyright {\textcopyright} 2005 John Wiley & Sons, Ltd.",

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AU - Lee, Andy H.

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AU - McLachlan, Geoffrey J.

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Y1 - 2006/5/30

N2 - To account for the preponderance of zero counts and simultaneous correlation of observations, a class of zero-inflated Poisson mixed regression models is applicable for accommodating the within-cluster dependence. In this paper, a score test for zero-inflation is developed for assessing correlated count data with excess zeros. The sampling distribution and the power of the test statistic are evaluated by simulation studies. The results show that the test statistic performs satisfactorily under a wide range of conditions. The test procedure is further illustrated using a data set on recurrent urinary tract infections. Copyright © 2005 John Wiley & Sons, Ltd.

AB - To account for the preponderance of zero counts and simultaneous correlation of observations, a class of zero-inflated Poisson mixed regression models is applicable for accommodating the within-cluster dependence. In this paper, a score test for zero-inflation is developed for assessing correlated count data with excess zeros. The sampling distribution and the power of the test statistic are evaluated by simulation studies. The results show that the test statistic performs satisfactorily under a wide range of conditions. The test procedure is further illustrated using a data set on recurrent urinary tract infections. Copyright © 2005 John Wiley & Sons, Ltd.

KW - Count data

KW - Poisson mixed model

KW - Power

KW - Score test

KW - Zero-inflation

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DO - 10.1002/sim.2308

M3 - RGC 21 - Publication in refereed journal

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VL - 25

SP - 1660

EP - 1671

JO - Statistics in Medicine

JF - Statistics in Medicine

IS - 10

ER -

A score test for zero-inflation in correlated count data

Abstract

Research Keywords

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