Modeling data with zero inflation and overdispersion using GAMLSSs

TL;DR

This paper demonstrates how GAMLSSs can effectively model zero-inflated and overdispersed count data, providing flexible analysis tools that improve model comparison and interpretability.

Contribution

It introduces the application of GAMLSSs to zero-inflated data, highlighting their flexibility beyond traditional exponential family models.

Findings

GAMLSSs effectively model zero-inflated count data.

Enhanced model comparison using GAMLSSs tools.

Improved interpretability of results with GAMLSSs.

Abstract

Count data with high frequencies of zeros are found in many areas, specially in biology. Statistical models to analyze such data started to be developed in the 80s and are still a topic of active research. Such models usually assume a response distribution that belongs to the exponential family of distributions and the analysis is performed under the generalized linear models framework. However, the generalized additive models for location, scale and shape (GAMLSSs) represent a more general class of univariate models that can also be used to model zero inflated data. In this paper, the analysis of a data set with excess of zeros and overdispersion is described using GAMLSSs. Specific GAMLSSs' tools were used in the analysis, which enhanced model comparison and eased the interpretation of results.