Bayesian analysis of measurement error models using INLA

2013/02/13 by Stefanie Muff, Andrea Riebler, Muff, Stefanie +7
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1302.3065

openalex publication_date 2013/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

To account for measurement error (ME) in explanatory variables, Bayesian approaches provide a flexible framework, as expert knowledge about unobserved covariates can be incorporated in the prior distributions. However, given the analytic intractability of the posterior distribution, model inference so far has to be performed via time-consuming and complex Markov chain Monte Carlo implementations. In this paper we extend the Integrated nested Laplace approximations (INLA) approach to formulate Gaussian ME models in generalized linear mixed models. We present three applications, and show how parameter estimates are obtained for common ME models, such as the classical and Berkson error model including heteroscedastic variances. To illustrate the practical feasibility, R-code is provided.

Citations

• Extending INLA to a class of near-Gaussian latent models

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