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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Valle, Denis [1] ; Ben Toh, Kok [2] ; Laporta, Gabriel Zorello [3, 4] ; Zhao, Qing [1]
Total Authors: 4
[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
Total Affiliations: 4
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 9, FEB 28 2019.
Web of Science Citations: 1

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)

FAPESP's process: 14/09774-1 - Dynamics of malaria transmission under distinct landscape fragmentation thresholds
Grantee:Gabriel Zorello Laporta
Support type: BIOTA-FAPESP Program - Young Investigators Grants