MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness

DOI10.1016/j.csda.2010.04.026zbMath1247.62142OpenAlexW2064262661MaRDI QIDQ452575

Michael G. Kenward, Cristina Sotto, Caroline Beunckens, Geert Molenberghs

Publication date: 15 September 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2010.04.026

zbMATH Keywords

EM algorithmMarkov chain Monte Carlomultivariate Dale model

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

A comparison of various software tools for dealing with missing data via imputation, Discretization-based direct random sample generation

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The nature of sensitivity in monotone missing not at random models
• Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm
• Inference from iterative simulation using multiple sequences
• Models for discrete longitudinal data.
• The common structure of several models for non-ignorable dropout
• A stochastic approximation type EM algorithm for the mixture problem
• Inference and missing data
• Analysis of Longitudinal Data with Non-Ignorable Non-Monotone Missing Values
• Missing Data, Imputation, and the Bootstrap
• Simultaneously Modeling Joint and Marginal Distributions of Multivariate Categorical Responses
• Marginal Modeling of Correlated Ordinal Data Using a Multivariate Plackett Distribution
• Stochastic versions of the em algorithm: an experimental study in the mixture case
• Informative Drop-Out in Longitudinal Data Analysis
• Linear mixed models for longitudinal data