Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

2018 ◽  
Vol 374 ◽  
pp. 51-59 ◽  
Author(s):  
Adam Duarte ◽  
Michael J. Adams ◽  
James T. Peterson
Keyword(s):  
Mixture Models ◽  
Count Data ◽  
Alternative Approaches ◽  
Heterogeneity Bias

scholarly journals Disease‐structured N ‐mixture models: A practical guide to model disease dynamics using count data

2019 ◽  
Vol 9 (2) ◽  
pp. 899-909 ◽  
Author(s):  
Graziella V. DiRenzo ◽  
Christian Che‐Castaldo ◽  
Sarah P. Saunders ◽  
Evan H. Campbell Grant ◽  
Elise F. Zipkin
Keyword(s):  
Mixture Models ◽  
Count Data ◽  
Disease Dynamics ◽  
Practical Guide

Mixture Models for the Analysis of Repeated Count Data

1995 ◽  
Vol 44 (4) ◽  
pp. 473 ◽  
Author(s):  
Marijtje A. J. van Duijn ◽  
Ulf Bockenholt
Keyword(s):  
Mixture Models ◽  
Count Data

Large-scale monitoring of shorebird populations using count data andN-mixture models: Black Oystercatcher (Haematopus bachmani) surveys by land and sea

The Auk ◽  
2012 ◽  
Vol 129 (4) ◽  
pp. 645-652 ◽  
Author(s):  
James E. Lyons ◽  
J. Andrew Royle ◽  
Susan M. Thomas ◽  
Elise Elliott-Smith ◽  
Joseph R. Evenson ◽  
...  
Keyword(s):  
Mixture Models ◽  
Count Data ◽  
Large Scale

scholarly journals Marginalized mixture models for count data from multiple source populations

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Habtamu K. Benecha ◽  
Brian Neelon ◽  
Kimon Divaris ◽  
John S. Preisser
Keyword(s):  
Mixture Models ◽  
Count Data ◽  
Multiple Source ◽  
Source Populations

A Bayesian approach for some zero-modified Poisson mixture models

2019 ◽  
Vol 20 (5) ◽  
pp. 467-501
Author(s):  
Wesley Bertoli ◽  
Katiane S Conceição ◽  
Marinho G Andrade ◽  
Francisco Louzada
Keyword(s):  
Mixture Models ◽  
Bayesian Approach ◽  
Count Data ◽  
General Model ◽  
Model Comparison ◽  
Discrete Distributions ◽  
Simulation Studies ◽  
Relevant Feature ◽  
Poisson Mixture ◽  
Fully Bayesian

In this article, we propose a class of zero-modified Poisson mixture models as an alternative to model overdispersed count data exhibiting inflation or deflation of zeros. A relevant feature of this class is that the zero modification can be incorporated using a zero truncation process and consequently, the proposed models can be expressed in the hurdle version. This procedure leads to the fact that the proposed models can be fitted without any previous information about the zero modification present in agiven dataset. A fully Bayesian approach has been considered for estimation and inference concerns. Three different simulation studies have been conducted to illustrate the performance of the developed methodology. The usefulness of the proposed class of models has been assessed by using three real datasets provided by the literature. A general model comparison with some well-known discrete distributions has been presented.

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