In this study, two-state Markov switching count data models are proposed as an alternative to zero-inflated models, in order to account for preponderance of zeros typically observed in accident frequency data. Similar to zero-inflated models, two-state Markov switching models assume an existence of two states of roadway safety. One of the states is a zero-accident state, which is safe. The othe...

BACKGROUND Alcohol consumption is commonly used as a primary outcome in randomized alcohol treatment studies. The distribution of alcohol consumption is highly skewed, particularly in subjects with alcohol dependence. METHODS In this paper, we will consider the use of count models for outcomes in a randomized clinical trial setting. These include the Poisson, over-dispersed Poisson, negative ...

Background: The number of failed courses and semesters in students are indicatorsof their performance. These amounts have zero inflated (ZI) distributions. Using ZI Poisson and negative binomial distributions we can model these count data to find the associated factors and estimate the parameters. This study aims at to investigate the important factors related to the educational performance of ...

Recent studies show that soil animal count data are characterized by the presence of excess zeros and overdispersion, which violate the assumptions of standard statistical tests. Despite this, analyses have consisted of mainly non-parametric tests and log-normal least square regression (i.e. ANOVA). Failure to accommodate zero inflation in count data can result in biased estimation of ecologica...

Phenotypes measured in counts are commonly observed in nature. Statistical methods for mapping quantitative trait loci (QTL) underlying count traits are documented in the literature. The majority of them assume that the count phenotype follows a Poisson distribution with appropriate techniques being applied to handle data dispersion. When a count trait has a genetic basis, "naturally occurring"...

A Poisson model typically is assumed for count data, but when there are so many zeroes in the response variable, because of overdispersion, a negative binomial regression is suggested as a count regression instead of Poisson regression. In this paper, a zero-inflated negative binomial regression model with right truncation count data was developed. In this model, we considered a response variab...