Estimating overdispersion in sparse multinomial data.
Dirichlet-multinomial
lack-of-fit
mark-recapture
multinomial
overdispersion
sparse data
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
02
03
2019
revised:
11
11
2019
accepted:
14
11
2019
pubmed:
1
12
2019
medline:
26
10
2021
entrez:
1
12
2019
Statut:
ppublish
Résumé
Multinomial data arise in many areas of the life sciences, such as mark-recapture studies and phylogenetics, and will often by overdispersed, with the variance being higher than predicted by a multinomial model. The quasi-likelihood approach to modeling this overdispersion involves the assumption that the variance is proportional to that specified by the multinomial model. As this approach does not require specification of the full distribution of the response variable, it can be more robust than fitting a Dirichlet-multinomial model or adding a random effect to the linear predictor. Estimation of the amount of overdispersion is often based on Pearson's statistic X
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
834-842Subventions
Organisme : University of Otago
ID : PhD Scholarship
Informations de copyright
© 2019 The International Biometric Society.
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