Stochastic Algorithms for Markov Models Estimation with Intermittent Missing Data

DOI10.1111/j.0006-341X.1999.00565.xzbMath1059.62639WikidataQ30650158 ScholiaQ30650158MaRDI QIDQ4666609

Jean-Yves le Hesran, Sylvia Richardson, Isabelle Deltour

Publication date: 13 April 2005

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

EM algorithmlongitudinal dataMarkov chainGibbs samplingmalariaintermittent missing datanonignorable missing data by design

Mathematics Subject Classification ID

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

Intermittent Missing Observations in Discrete-Time Hidden Markov Models ⋮ Estimating discrete Markov models from various incomplete data schemes ⋮ Fitting feature-dependent Markov chains ⋮ A Transitional Model for Longitudinal Binary Data Subject to Nonignorable Missing Data ⋮ Analysis of Longitudinal Data with Irregular, Outcome-Dependent Follow-Up ⋮ Joint Modeling and Analysis of Longitudinal Data with Informative Observation Times ⋮ A Bayesian Model for Markov Chains via Jeffrey's Prior ⋮ A Flexible Parametric Model for Combining Current Status and Age at First Diagnosis Data ⋮ A Bayesian Approach in Estimating Transition Probabilities of a Discrete-time Markov Chain for Ignorable Intermittent Missing Data

Cites Work

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• Inoculation and recovery rates in the malaria model of Dietz, Molineaux, and Thomas
• A three-state Markov model of plasmodium falciparum parasitemia
• Inference from iterative simulation using multiple sequences
• Statistical Inference about Markov Chains
• A Markov Model for Analysing Cancer Markers and Disease States in Survival Studies
• Model-based Inference for Categorical Survey Data Subject to Non-ignorable Non-response
• Asymptotic properties of a stochastic EM Algorithm for estimating mixing proportions
• Missing Data, Imputation, and the Bootstrap
• Estimating a Markov Transition Matrix from Observational Data
• Informative Drop-Out in Longitudinal Data Analysis
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies