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

Autor(es)

Valle, Denis ^[1] ; Ben Toh, Kok ^[2] ; Laporta, Gabriel Zorello ^{[3, 4]} ; Zhao, Qing ^[1]

Afiliação do(s) autor(es)

^[1] Univ Florida, Sch Forest Resources & Conservat, Gainesville, FL 32611 - USA

^[2] Univ Florida, Sch Nat Resources & Environm, Gainesville, FL - USA

^[3] Univ Fed ABC, Ctr Engn Modelagem & Ciencias Sociais Aplicadas, Santo Andre, SP - Brazil

^[4] Fac Med ABC, Setor Posgrad Pesquisa & Inovacao, Santo Andre, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: SCIENTIFIC REPORTS; v. 9, FEB 28 2019.
• Citações Web of Science: 2

Resumo

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 malaria, are influenced by environmental factors in the Peruvian Amazon. The MN-MS model had one of the best fit and out-of-sample predictive skill amongst all models. While A. darlingi is strongly associated with highly anthropized landscapes, all the other mosquito species had higher mean biting rates in landscapes with a lower fraction of exposed soil and urban area, revealing a striking shift in species composition. We believe that the MN and MN-MS models are valuable additions to the modelling toolkit employed by environmental modelers and quantitative ecologists. (AU)

• Processo FAPESP: 14/09774-1 - Dinâmica de transmissão de malária em diferentes limiares de fragmentação da paisagem
• Beneficiário: Gabriel Zorello Laporta
• Modalidade de apoio: Auxílio à Pesquisa - Programa BIOTA - Jovens Pesquisadores