A continuous time-and-state epidemic model fitted to ordinal categorical data observed on a lattice at discrete times

DOI10.1007/s13253-013-0138-xzbMath1303.62065OpenAlexW1993213507WikidataQ58993075 ScholiaQ58993075MaRDI QIDQ486076

Rémi Crété, Valérie Caffier, Besnik Pumo, Frédérique Didelot, Samuel Soubeyrand

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/s13253-013-0138-x

zbMATH Keywords

Markov chain Monte CarloBayesian inferencecategorical datamultivariate point processspatial-temporal processapple scab

Mathematics Subject Classification ID

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

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Inference from iterative simulation using multiple sequences
• Practical Markov Chain Monte Carlo
• Spatial-Temporal Modeling of Forest Gaps Generated by Colonization From Below- and Above-Ground Bark Beetle Species
• Markov Chain Monte Carlo Methods for Fitting Spatiotemporal Stochastic Models in Plant Epidemiology
• An Introduction to the Theory of Point Processes
• Space-time estimation of a particle system model
• Statistical Analysis and Modelling of Spatial Point Patterns

This page was built for publication: A continuous time-and-state epidemic model fitted to ordinal categorical data observed on a lattice at discrete times