Composite likelihood for aggregate data from clustered multistate processes under intermittent observation

DOI10.1080/03610926.2019.1584310OpenAlexW2921173295WikidataQ128219734 ScholiaQ128219734MaRDI QIDQ5077232

Shu Jiang, Richard J. Cook

Publication date: 18 May 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10012/16657

zbMATH Keywords

Markov processheterogeneityclustered datarandom effect modelaggregate dataintermittent observation

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• msm
• invGauss

Cites Work

• Estimation from aggregate data
• An introduction to copulas.
• Survival and event history analysis. A process point of view
• An Empirical Distribution Function for Sampling with Incomplete Information
• A Conditional Markov Model for Clustered Progressive Multistate Processes under Incomplete Observation
• The Analysis of Panel Data Under a Markov Assumption
• Multistate Models for the Analysis of Life History Data
• Estimating the Extent of Tracking in Interval‐Censored Chain‐Of‐Events Data
• Transition Models for Multivariate Longitudinal Binary Data
• Maximum Likelihood Estimation of Misspecified Models
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