A Bayesian semiparametric model for case-control studies with errors in variables

DOI10.1093/biomet/84.3.523zbMath0888.62023OpenAlexW2001220785MaRDI QIDQ4364921

Kathryn Roeder, Peter Mueller

Publication date: 8 June 1998

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/574387e01cf0a7f0232d26c76d585ae32728246a

zbMATH Keywords

Markov chain Monte Carlologistic regressionmeasurement errorGibbs samplingmixture of Dirichlet processesMetropolis samplingcholesterol study

Mathematics Subject Classification ID

Density estimation (62G07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Probabilistic methods, stochastic differential equations (65C99)

Related Items (18)

Mixture Models in Measurement Error Problems, with Reference to Epidemiological Studies ⋮ Analysis of Longitudinal Data in the Case–Control Studies via Empirical Likelihood ⋮ Semiparametric Bayesian measurement error modeling ⋮ Semiparametric errors-in-variables models. A Bayesian approach ⋮ Multivariate measurement error models using finite mixtures of skew-Student \(t\) distributions ⋮ Influential Observations in the Functional Measurement Error Model ⋮ Generalized linear models with covariate measurement error and unknown link function ⋮ Parametric Bayesian analysis of case-control data with imprecise exposure measurements ⋮ Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error ⋮ Measurement error correction using validation data: a review of methods and their applicability in case-control studies ⋮ A Bayesian Hierarchical Approach for Combining Case‐Control and Prospective Studies ⋮ A flexible model for generalized linear regression with measurement error ⋮ Non‐parametric Estimation of a Survival Function with Two‐stage Design Studies ⋮ Case-Control Studies of Gene-Environment Interaction: Bayesian Design and Analysis ⋮ Nonparametric Bayes modeling for case control studies with many predictors ⋮ Semiparametric Regression Modeling with Mixtures of Berkson and Classical Error, with Application to Fallout from the Nevada Test Site ⋮ Measurement error models: from nonparametric methods to deep neural networks ⋮ Objective Priors for Parameters in a Normal Linear Regression with Measurement Error

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