This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a new zero-inflated count data model that is based on two homogeneous Poisson processes and accounts for exposure time in a theory consistent way. The new model is used in an application to the effect of insurance generosity on the number of absent days. JEL Classification: J29, C25

We present `latentcor`, an R package for correlation estimation from data with mixed variable types. Mixed variables types, including continuous, binary, ordinal, zero-inflated, or truncated are routinely collected in many areas of science. Accurate correlations among such is often the first critical step statistical analysis workflows. Pearson as default choice not well suited types underlying...

Abstract Smoking invariably has environmental, social, economic and health consequences in Ethiopia. Reducing quitting cigarette smoking improves individual increases available household funds for education, food better productivity. Therefore, this study aimed to apply the Bayesian negative binomial logit hurdle zero-inflated model determine associated factors of number smokers per day using i...

Counting outcomes such as days of physical activity or servings of fruits and vegetables often have distributions that are highly skewed toward the right with a preponderance of zeros, posing analytical challenges. This paper demonstrates how such outcomes may be analyzed with several modifications to Poisson regression. Five regression models 1) Poisson, 2) overdispersed Poisson, 3) negative b...

Composite links and exploded likelihoods are powerful yet simple tools for specifying a wide range of latent variable models. Applications considered include survival or duration models, models for rankings, small area estimation with census information, models for ordinal responses, item response models with guessing, randomized response models, unfolding models, latent class models with rando...