Residuals in Bell-Touchard regression models
Residual Analysis. Regression Model. Generalized Linear Models. Count Data. Pearson Residual.
The Bell-Touchard distribution and its regression model, are a promising alternative to the statistical models that are already established in the count data modeling, for example, Poisson and Negative-Binomial models since it could be verified in many situations, a better fit to the data through AIC method and by residual analysis. Count data are observations that assume non-negative integer values and are useful, for example, to modeling the number of claims that happened in a given period of time, which are of interest to insurance companies, for example. The Poisson model, in this regard, is not a model that does not work well with the overdispersion problem, a situation where the variance is greater than the expectation. Alternatives to deal with this kind of problem emerged, being proposed the zero-inflated models and the negative-binomial model. The Bell-Touchard model is a generalization of the Bell model, and it is a recent alternative that tries to solve this kind of problem. Deeper research is made about alternative residuals to the Bell-Touchard, and the main goal is to improve the academic literature about count data.