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.

The power and pitfalls of Dirichlet-multinomial mixture models for ecological count data

2016 ◽  
Author(s):  
John D. O’Brien ◽  
Nicholas R. Record ◽  
Peter Countway
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Mixture Models ◽  
Mixture Model ◽  
Count Data ◽  
Empirical Data ◽  
Infinite Mixture Model ◽  
Biological Interpretation ◽  
Complex Models ◽  
The Many

AbstractThe Dirichlet-multinomial mixture model (DMM) and its extensions provide powerful new tools for interpreting the ecological dynamics underlying taxon abundance data. However, like many complex models, how effectively they capture the many features of empirical data is not well understood. In this work, we expand the DMM to an infinite mixture model (iDMM) and use posterior predictive distributions (PPDs) to explore the performance in three case studies, including two amplicon metagenomic time series. We avoid concentrating on fluctuations within individual taxa and instead focus on consortial-level dynamics, using straight-forward methods for visualizing this perspective. In each study, the iDMM appears to perform well in organizing the data as a framework for biological interpretation. Using the PPDs, we also observe several exceptions where the data appear to significantly depart from the model in ways that give useful ecological insight. We summarize the conclusions as a set of considerations for field researchers: problems with samples and taxa; relevant scales of ecological fluctuation; additional niches as outgroups; and possible violations of niche neutrality.

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