Mixture models for overdispersed data

Rhodes, Jonathan R. (2015). Mixture models for overdispersed data. In Gordon A. Fox, Simoneta Negrete-Yankelevich and Vinicio J. Sosa (Ed.), Ecological statistics: contemporary theory and application (pp. 284-308) Oxford, United Kingdom: Oxford University Press. doi:10.1093/acprof:oso/9780199672547.001.0001

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Author Rhodes, Jonathan R.
Title of chapter Mixture models for overdispersed data
Title of book Ecological statistics: contemporary theory and application
Place of Publication Oxford, United Kingdom
Publisher Oxford University Press
Publication Year 2015
Sub-type Research book chapter (original research)
DOI 10.1093/acprof:oso/9780199672547.001.0001
Open Access Status Not Open Access
ISBN 9780199672554
Editor Gordon A. Fox
Simoneta Negrete-Yankelevich
Vinicio J. Sosa
Chapter number 12
Start page 284
End page 308
Total pages 25
Total chapters 13
Collection year 2016
Language eng
Abstract/Summary Ecological data often do not conform to the assumptions of standard probability distributions and this has important implications for the validity of statistical inference. A common reason for this is that the variability of ecological data is often much higher than can be accounted for by the standard probability distributions that underpin most statistical inference in ecology. This leads to an underestimation of variances and bias in statistical tests unless the overdispersion is accounted for. Consequently, having methods for dealing with overdispersion is an essential component of the ecologist’s statistical toolbox. This chapter introduces statistical methods known as mixture models that can deal with overdispersion. Mixture models are powerful because not only can they account for overdispersion, but they can also help to identify the actual ecological or observation processes that drive overdispersion. The chapter begins by discussing the causes and consequences of overdispersion in ecological data and how overdispersion can be identified. Mixture models are then described and illustrated using two different case studies from survival analysis and the analysis of population abundance. The chapter ends with a discussion of some of the limitations of mixture models and pitfalls to look out for.
Keyword Continuous mixture model
Countable mixture model
Finite mixture model
Mixture model
Observation error
Q-Index Code B1
Q-Index Status Confirmed Code
Institutional Status UQ

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