Sequential Monte Carlo Methods in Random Intercept Models for Longitudinal Data

DOI10.1007/978-3-319-54084-9_1zbMath1364.62253OpenAlexW2608900305MaRDI QIDQ5267849

Danilo Alvares, Carmen Armero, Nicolas Chopin, Anabel Forte

Publication date: 13 June 2017

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-54084-9_1

zbMATH Keywords

Bayesian analysismarginal likelihoodparticle filtersequential Monte Carlo methodslongitudinal modellingIBIS algorithmbiostatistical research

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Monte Carlo methods (65C05)

Uses Software

• WinBUGS
• BayesDA

Cites Work

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• Random-Effects Models for Longitudinal Data
• Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models
• Sequential Monte Carlo Methods in Practice
• Joint Models for Longitudinal and Time-to-Event Data
• A sequential particle filter method for static models

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