A stochastic variant of the EM algorithm to fit mixed (discrete and continuous) longitudinal data with nonignorable missingness

DOI10.1080/03610926.2019.1601223OpenAlexW2937443283MaRDI QIDQ5077518

Abdallah S. A. Yaseen, Ahmed M. Gad

Publication date: 18 May 2022

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

Full work available at URL: https://doi.org/10.1080/03610926.2019.1601223

zbMATH Keywords

longitudinal datamaximum likelihoodmissing datastochastic EMparametric fractional imputationinterstitial cystitis data

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Parametric fractional imputation for missing data analysis
• Parametric fractional imputation for nonignorable missing data
• Missing data methods in longitudinal studies: a review
• Multivariate negative binomial models for insurance claim counts
• Parametric fractional imputation for mixed models with nonignorable missing data
• Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm
• Joint analysis of mixed Poisson and continuous longitudinal data with nonignorable missing values
• Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable
• Imputation for statistical inference with coarse data
• Unbalanced Repeated-Measures Models with Structured Covariance Matrices
• Maximum likelihood estimation via the ECM algorithm: A general framework
• An EM algorithm for multivariate Poisson distribution and related models
• Marginal models for the analysis of longitudinal measurements with nonignorable non-monotone missing data
• Regression Models for Mixed Discrete and Continuous Responses with Potentially Missing Values
• Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates
• Informative Drop-Out in Longitudinal Data Analysis
• A review of multivariate longitudinal data analysis
• Multivariate Correlation Models with Mixed Discrete and Continuous Variables

This page was built for publication: A stochastic variant of the EM algorithm to fit mixed (discrete and continuous) longitudinal data with nonignorable missingness