Ordinal regression models for zero-inflated and/or over-dispersed count data.

ABSTRACT: Count data commonly arise in natural sciences but adequately modeling these data is challenging due to zero-inflation and over-dispersion. While multiple parametric modeling approaches have been proposed, unfortunately there is no consensus regarding how to choose the best model. In this article, we propose a ordinal regression model (MN) as a default model for count data given that this model is shown to fit well data that arise from several types of discrete distributions. We extend this model to allow for automatic model selection (MN-MS) and show that the MN-MS model generates superior inference when compared to using the full model or more traditional model selection approaches. The MN-MS model is used to determine how human biting rate of mosquitoes, known to be able to transmit mala

SUBMITTER: Valle D 

PROVIDER: S-EPMC6395857 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature