The Poisson regression model is widely used for count data. This assumes equidispersion. In practice, equidispersion seldom reflected in However, real-life data, the variance usually exceeds mean. situation known as overdispersion. Negative binomial distribution and other mix models are often to overdispersion Another extension of negative another data univariate generalized Waring. addition, d...

Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population. Rec...

BACKGROUND Traditional statistical models often are based on certain presuppositions and limitations that may not presence in actual data and lead to turbulence in estimation or prediction. In these situations, artificial neural networks (ANNs) could be suitable alternative rather than classical statistical methods. STUDY DESIGN  A prospective cohort study. METHODS The study was conducted i...

Count data are increasingly ubiquitous in genetic association studies, where it is possible to observe excess zero counts as compared to what is expected based on standard assumptions. For instance, in rheumatology, data are usually collected in multiple joints within a person or multiple sub-regions of a joint, and it is not uncommon that the phenotypes contain enormous number of zeroes due to...

Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can b...

A number of mixture modeling approaches assume both normality and independent observations. However, these two assumptions are at odds with the reality of many data sets, which are often characterized by an abundance of zero-valued or highly skewed observations as well as observations from biologically related (i.e., non-independent) subjects. We present here a finite mixture model with a zero-...