Score tests for zero inflation in generalized linear models

Deng, Dianliang; Paul, S. R.

doi:10.2307/3315965

Cited by 59 publications

(71 citation statements)

References 11 publications

(12 reference statements)

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“…The derivation of this score S is in principle the same as the score statistic Z 2 proposed by Deng and Paul [22] which does not involve random effects. In the following, all expectations are taken under H 0 : = 0 and conditional on random effects u and v. Based on the second derivatives of l evaluated at the REML estimates, the expected Fisher information matrix is partitioned as…”

Section: Score Test For Overdispersionmentioning

confidence: 99%

A score test for overdispersion in zero‐inflated poisson mixed regression model

Xiang

Lee

Yau

et al. 2006

Statistics in Medicine

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Count data with extra zeros are common in many medical applications. The zero-inflated Poisson (ZIP) regression model is useful to analyse such data. For hierarchical or correlated count data where the observations are either clustered or represent repeated outcomes from individual subjects, a class of ZIP mixed regression models may be appropriate. However, the ZIP parameter estimates can be severely biased if the non-zero counts are overdispersed in relation to the Poisson distribution. In this paper, a score test is proposed for testing the ZIP mixed regression model against the zero-inflated negative binomial alternative. Sampling distribution and 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 applied to pancreas disorder length of stay that comprised mainly same-day separations and simultaneous prolonged hospitalizations.

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Section: Score Test For Overdispersionmentioning

confidence: 99%

A score test for overdispersion in zero‐inflated poisson mixed regression model

Xiang

Lee

Yau

et al. 2006

Statistics in Medicine

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“…This mixture distribution has become the foundation of much methodological development in zero-inflated count data analysis. Some authors have made the inferences on the existence of zero inflation in the count data (e.g., El-Shaarawi, 1985;van den Broek, 1995;Deng & Paul, 2000;Ridout, Hinde, & Demétrio, 2001;Jansakul & Hinde, 2002;Thas & Rayner, 2005); others have constructed various ZIP regression models. The seminal work on ZIP regression by Lambert (1992) was used to model the extra proportion of zeros π and the mean of the Poisson distribution λ simultaneously with linear predictors using the appropriate link functions, and the parametric ZIP regression model was applied to the manufacturing data.…”

Section: Introductionmentioning

confidence: 99%

Score Tests for Semiparametric Zero-inflated Poisson Models

Li¹

2012

IJSP

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Count data sets often produce many zeros. It is sometimes potentially questionable to use a linear predictor to model the effect of a continuous covariate of interest in zero-inflated count data. To relax the restriction, Li (2011) proposed a semiparametric zero-inflated Poisson (ZIP) regression model by using fixed-knot cubic basis splines or B-splines to model the covariate effect, and used the likelihood ratio test to assess the validity of the linear relationship between the natural logarithm of the Poisson mean and the covariate. A score test is conducted to assess whether the extra proportion of zeros in the semiparametric ZIP regression model is equal to zero.

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“…In the literature, tests for zero-inflation have been developed for independent data. [15][16][17]34 A score test for correlated count data is outlined below. The advantage of the score statistic lies in its computational convenience; only a fit of the null Poisson mixed model is required.…”

Section: Score Test For Zero-inflationmentioning

confidence: 99%

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Lee

Wang

Jordan

et al. 2006

Stat Methods Med Res

191

151

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Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which render the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

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Score tests for zero inflation in generalized linear models

Cited by 59 publications

References 11 publications

A score test for overdispersion in zero‐inflated poisson mixed regression model

A score test for overdispersion in zero‐inflated poisson mixed regression model

Score Tests for Semiparametric Zero-inflated Poisson Models

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

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