Bayesian analysis of multivariate probit models with surrogate outcome data

DOI10.1007/s11336-010-9164-6zbMath1208.62193OpenAlexW2029660590MaRDI QIDQ603165

Wai-Yin Poon, Hai-Bin Wang

Publication date: 5 November 2010

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-010-9164-6

zbMATH Keywords

Gibbs samplererrors-in-variablesMetropolis-Hastings algorithmmisclassificationparameter expansionsurrogate variable

Mathematics Subject Classification ID

Related Items (9)

A Bayesian approach for misclassified ordinal response data ⋮ Bayesian estimation of logistic regression with misclassified covariates and response ⋮ Confidence intervals for an ordinal effect size measure based on partially validated series ⋮ Estimation of Poisson mean with under‐reported counts: a double sampling approach ⋮ Confidence interval construction for disease prevalence based on partial validation series ⋮ A unified framework for the comparison of treatments with ordinal responses ⋮ Testing of Poisson mean with under-reported counts ⋮ Analysis of ordinal longitudinal data under nonignorable missingness and misreporting: an application to Alzheimer's disease study ⋮ Analysis of ordered probit model with surrogate response data and measurement error in covariates

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Likelihood-based kernel estimation in semiparametric errors-in-covariables models with validation data
• Auxiliary outcome data and the mean score method
• Inference from iterative simulation using multiple sequences
• Markov chains for exploring posterior distributions. (With discussion)
• Random Effects Logistic Regression Analysis with Auxiliary Covariates
• Likelihood inference in a correlated probit regression model
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Calculation of Posterior Distributions by Data Augmentation
• Precision of double sampling estimators for comparing two probabilities
• Inference using surrogate outcome data and a validation sample
• Analysis of multivariate probit models
• Parameter expansion to accelerate EM: the PX-EM algorithm
• Seeking efficient data augmentation schemes via conditional and marginal augmentation
• High-Dimensional Multivariate Probit Analysis
• Modeling Discrete Choice With Response Error: Food Stamp Participation
• Empirical likelihood-based inference in linear errors-in-covariables models with validation data
• Miscellanea. A robust imputation method for surrogate outcome data
• Parameter Expansion for Data Augmentation
• Estimating from Misclassified Data
• Monte Carlo sampling methods using Markov chains and their applications

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