A Mixed Mover–Stayer Model for Spatiotemporal Two‐State Processes

DOI10.1111/j.1541-0420.2007.00752.xzbMath1146.62096OpenAlexW1964110958WikidataQ51906416 ScholiaQ51906416MaRDI QIDQ5434921

Farouk S. Nathoo, C. B. Dean

Publication date: 14 January 2008

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2007.00752.x

zbMATH Keywords

intrinsic autoregressive modelMonte Carlo EM algorithmmixed Markov modelspatiotemporal binary data

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (3)

Zero-Inflated Spatial Models: Application and Interpretation ⋮ Joint Spatial Modeling of Recurrent Infection and Growth with Processes under Intermittent Observation ⋮ Spatial Multistate Transitional Models for Longitudinal Event Data

Uses Software

• spBayes

Cites Work

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• Publication Bias and Meta-Analysis for 2×2 Tables: An Average Markov Chain Monte Carlo EM Algorithm
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• Monte Carlo EM Estimation for Time Series Models Involving Counts
• Ascent-Based Monte Carlo Expectation– Maximization
• A generalized mover-stayer model for panel data
• Proper multivariate conditional autoregressive models for spatial data analysis

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