Count data often show a higher incidence of zero counts than would be expected if the data were Poisson distributed. Zero-inflated Poisson regression models are a useful class of models for such data, but parameter estimates may be seriously biased if the nonzero counts are overdispersed in relation to the Poisson distribution. We therefore provide a score test for testing zero-inflated Poisson regression models against zero-inflated negative binomial alternatives
Excess zeros and overdispersion are commonly encountered phenomena that limit the use of traditional...
Count data often exhibit overdispersion and/or require an adjustment for zero outcomes with respect...
Researchers in many fields including biomedical often make statistical inferences involving the anal...
Count data with extra zeros are common in many medical applications. The zero-inflated Poisson (ZIP)...
For a random variable y representing counts where sample mean and sample variance are equal, the Poi...
Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151-5165, 2014) ...
In many biomedical applications, count data have a large proportion of zeros and the zero-inflated P...
WOS:000822397600012Count data regression has been widely used in various disciplines, particularly h...
The Poisson regression model remains an important tool in the econometric analysis of count data. In...
In this study we focus on a negative binomial (NB) regression model to take account of regression co...
A natural approach to analyzing the effect of covariates on a count response variable is to use a P...
If one uses the observed information matrix, rather than the expected information matrix in a score ...
Researchers often encounter data which exhibit an excess number of zeroes than would be expected in ...
A Poisson regression model is commonly used to model count data. The Poisson model assumes equidispe...
We present several modifications of the Poisson and negative binomial models for count data to accom...
Excess zeros and overdispersion are commonly encountered phenomena that limit the use of traditional...
Count data often exhibit overdispersion and/or require an adjustment for zero outcomes with respect...
Researchers in many fields including biomedical often make statistical inferences involving the anal...
Count data with extra zeros are common in many medical applications. The zero-inflated Poisson (ZIP)...
For a random variable y representing counts where sample mean and sample variance are equal, the Poi...
Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151-5165, 2014) ...
In many biomedical applications, count data have a large proportion of zeros and the zero-inflated P...
WOS:000822397600012Count data regression has been widely used in various disciplines, particularly h...
The Poisson regression model remains an important tool in the econometric analysis of count data. In...
In this study we focus on a negative binomial (NB) regression model to take account of regression co...
A natural approach to analyzing the effect of covariates on a count response variable is to use a P...
If one uses the observed information matrix, rather than the expected information matrix in a score ...
Researchers often encounter data which exhibit an excess number of zeroes than would be expected in ...
A Poisson regression model is commonly used to model count data. The Poisson model assumes equidispe...
We present several modifications of the Poisson and negative binomial models for count data to accom...
Excess zeros and overdispersion are commonly encountered phenomena that limit the use of traditional...
Count data often exhibit overdispersion and/or require an adjustment for zero outcomes with respect...
Researchers in many fields including biomedical often make statistical inferences involving the anal...