Hierarchical Bayesian spatio-temporal Conway-Maxwell Poisson models with dynamic dispersion

DOI10.1007/s13253-013-0141-2zbMath1303.62100OpenAlexW2050963821MaRDI QIDQ486047

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

Publication date: 14 January 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-013-0141-2

zbMATH Keywords

count datanonlinearoverdispersionhierarchical modelkernel principal component analysisunderdispersionempirical orthogonal functionsthreshold vector autoregressive model

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (8)

Count Time Series: A Methodological Review ⋮ Generalised likelihood profiles for models with intractable likelihoods ⋮ Estimating reproduction and survival of unmarked juveniles using aerial images and marked adults ⋮ Bayesian Hierarchical Models With Conjugate Full-Conditional Distributions for Dependent Data From the Natural Exponential Family ⋮ Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion) ⋮ Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion ⋮ Bayesian binomial mixture models for estimating abundance in ecological monitoring studies ⋮ A Time-Series Model for Underdispersed or Overdispersed Counts

Uses Software

• R
• COUNT

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