Bayesian analysis of nonlinear structural equation models with nonignorable missing data

DOI10.1007/s11336-006-1177-1zbMath1306.62459OpenAlexW2138135286WikidataQ57535852 ScholiaQ57535852MaRDI QIDQ2260978

Sik-Yum Lee

Publication date: 6 March 2015

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-006-1177-1

zbMATH Keywords

path samplingBayes factorGibbs samplerMetropolis-Hastings algorithmmodel comparisonnonignorable missing data

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (19)

Statistical inference for nonignorable missing-data problems: a selective review ⋮ Semiparametric propensity weighting for nonignorable nonresponse: a discussion of ‘Statistical inference for nonignorable missing data problems: a selective review’ by Niansheng Tang and Yuanyuan Ju ⋮ Generalized empirical likelihood for nonsmooth estimating equations with missing data ⋮ Bayesian analysis of non-linear structural equation models with non-ignorable missing outcomes from reproductive dispersion models ⋮ Bayesian methods for dealing with missing data problems ⋮ Bayesian local influence analysis of general estimating equations with nonignorable missing data ⋮ Bayesian adaptive Lasso for quantile regression models with nonignorably missing response data ⋮ A semiparametric Bayesian approach to generalized partial linear mixed models for longitudinal data ⋮ Semiparametric Bayesian inference on generalized linear measurement error models ⋮ Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data ⋮ A heterogeneous Bayesian regression model for cross-sectional data involving a single observation per response unit ⋮ Unnamed Item ⋮ Bayesian analysis of semiparametric reproductive dispersion mixed-effects models ⋮ Bayesian quantile regression with mixed discrete and nonignorable missing covariates ⋮ On confidence regions of semiparametric nonlinear reproductive dispersion models ⋮ Local influence analysis of nonlinear structural equation models with nonignorable missing outcomes from reproductive dispersion models ⋮ Bayesian Analysis of Nonlinear Reproductive Dispersion Mixed Models for Longitudinal Data with Nonignorable Missing Covariates ⋮ Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model ⋮ Identifying variables responsible for data not missing at random

Uses Software

• R
• R2WinBUGS
• BayesDA

Cites Work

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• Random-Effects Models for Longitudinal Data
• Heterogeneous factor analysis models: a Bayesian approach
• Maximum likelihood estimation of nonlinear structural equation models
• Analysis of structural equation model with ignorable missing continuous and polytomous data
• Estimation for structural equation models with missing data
• Maximum likelihood estimation and model comparison for mixtures of structural equation models with ignorable missing data
• Bayesian estimation and testing of structural equation models
• Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable
• Practical Markov Chain Monte Carlo
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Calculation of Posterior Distributions by Data Augmentation
• Approximate Bayes factors and accounting for model uncertainty in generalised linear models
• Parameter Estimation from Incomplete Data in Binomial Regression When the Missing Data Mechanism is Nonignorable
• Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
• Estimation for Polynomial Structural Equation Models
• P Values for Composite Null Models
• Informative Drop-Out in Longitudinal Data Analysis
• Bayes Factors
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling

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