Re-sampling Techniques in Count Data Regression Models

Modeling count variables is a common task in many application areas such as economics, social sciences, and medicine. The classical Poisson regression model for count data is often used and it is limited in these disciplines since count data sets typically exhibit overdispersion, so negative binomial regression can be used. We use a jackknife- after- bootstrap procedure to assess the error in the bootstrap estimated parameters. The method is illustrated through two real examples. The results suggest that the jackknife- after- bootstrap method provides a reliable alternative to traditional methods particularly in small to moderate samples.