Bayesian binomial mixture models for estimating abundance in ecological monitoring studies

DOI10.1214/14-AOAS801zbMath1454.62467arXiv1505.02590MaRDI QIDQ2349555

Guohui Wu, Scott H. Holan, Charles H. Nilon, Christopher K. Wikle

Publication date: 17 June 2015

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1505.02590

zbMATH Keywords

parallel computingoverdispersionnegative binomialunderdispersionunbalanced dataAmerican Community SurveyAmerican RobinConway-Maxwell Poison

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (4)

Approximate Bayesian computation for finite mixture models ⋮ Bayesian models for spatial count data with informative finite populations with application to the American community survey ⋮ Joint Bayesian analysis of multiple response-types using the hierarchical generalized transformation model ⋮ Bayesian binomial mixture models for estimating abundance in ecological monitoring studies

Uses Software

• R
• SemiPar
• fields
• FRK

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Models for Estimating Abundance from Repeated Counts of an Open Metapopulation
• Hierarchical Bayesian spatio-temporal Conway-Maxwell Poisson models with dynamic dispersion
• Bayesian spatial modeling of data from avian point count surveys
• Semiparametric geographically weighted response curves with application to site-specific agriculture
• Bayesian binomial mixture models for estimating abundance in ecological monitoring studies
• Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love
• An empirical model for underdispersed count data
• N ‐Mixture Models for Estimating Population Size from Spatially Replicated Counts
• Fixed Rank Kriging for Very Large Spatial Data Sets
• Semiparametric Regression
• Bayesian model discrimination for multiple strata capture-recapture data
• Bayesian Measures of Model Complexity and Fit
• The COM‐Poisson model for count data: a survey of methods and applications
• A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution
• A review of Bayesian variable selection methods: what, how and which
• Conjugate analysis of the Conway-Maxwell-Poisson distribution

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