Statistical modeling of longitudinal data with non-ignorable non-monotone missingness with semiparametric Bayesian and machine learning components

DOI10.1007/s13571-019-00222-wzbMath1469.62202OpenAlexW3012317150MaRDI QIDQ2040667

Yu Cao, Nitai D. Mukhopadhyay

Publication date: 14 July 2021

Published in: Sankhyā. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13571-019-00222-w

zbMATH Keywords

imputationmissing dataBayesian nonparametric analysisnon-ignorable missingnessnon-monotone missingness

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Missing data (62D10)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples
• Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable
• Bayesian Nonparametrics for Missing Data in Longitudinal Clinical Trials
• Nonparametric sparsity and regularization
• Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model
• A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times
• Estimation of Regression Models for the Mean of Repeated Outcomes Under Nonignorable Nonmonotone Nonresponse
• Inference and missing data
• Monotone missing data and pattern‐mixture models
• A class of pattern-mixture models for normal incomplete data
• Pattern-Mixture Models for Multivariate Incomplete Data with Covariates
• Bayesian analysis of covariance matrices and dynamic models for longitudinal data
• Informative Drop-Out in Longitudinal Data Analysis
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies
• A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial
• Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

This page was built for publication: Statistical modeling of longitudinal data with non-ignorable non-monotone missingness with semiparametric Bayesian and machine learning components