Modelling zero-inflated spatio-temporal processes

DOI10.1177/1471082X0800900102OpenAlexW2101679955MaRDI QIDQ4970902

Helio S. Migon, Marcus V. M. Fernandes, Alexandra Mello Schmidt

Publication date: 7 October 2020

Published in: Statistical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/1471082x0800900102

zbMATH Keywords

Gaussian processesmixture modelsmodel comparisonBayesian paradigmconditional autoregressive processes

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Unnamed Item ⋮ Location Multiplicative Error Models with Quasi Maximum Likelihood Estimation ⋮ Generalized spatial dynamic factor models ⋮ Dynamic quantile linear models: a Bayesian approach

Uses Software

• spBayes

Cites Work

• Bayesian forecasting and dynamic models.
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• An efficient sampling scheme for dynamic generalized models
• Structured additive regression for overdispersed and zero-inflated count data
• Bayesian spatio-temporal models based on discrete convolutions
• Dynamic Generalized Linear Models and Bayesian Forecasting
• Model choice: a minimum posterior predictive loss approach
• Markov chain Monte Carlo for dynamic generalised linear models
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• A Nonstationary Multisite Model for Rainfall
• Adaptive Rejection Sampling for Gibbs Sampling
• A dimension-reduced approach to space-time Kalman filtering
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