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Stat Comput. 2016 Jul;26(4):827-840. Epub 2015 Jun 14.

Exact sampling of the unobserved covariates in Bayesian spline models for measurement error problems.

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Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, IN 47907-2066, USA.
Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143, USA.


In truncated polynomial spline or B-spline models where the covariates are measured with error, a fully Bayesian approach to model fitting requires the covariates and model parameters to be sampled at every Markov chain Monte Carlo iteration. Sampling the unobserved covariates poses a major computational problem and usually Gibbs sampling is not possible. This forces the practitioner to use a Metropolis-Hastings step which might suffer from unacceptable performance due to poor mixing and might require careful tuning. In this article we show for the cases of truncated polynomial spline or B-spline models of degree equal to one, the complete conditional distribution of the covariates measured with error is available explicitly as a mixture of double-truncated normals, thereby enabling a Gibbs sampling scheme. We demonstrate via a simulation study that our technique performs favorably in terms of computational efficiency and statistical performance. Our results indicate up to 62 and 54 % increase in mean integrated squared error efficiency when compared to existing alternatives while using truncated polynomial splines and B-splines respectively. Furthermore, there is evidence that the gain in efficiency increases with the measurement error variance, indicating the proposed method is a particularly valuable tool for challenging applications that present high measurement error. We conclude with a demonstration on a nutritional epidemiology data set from the NIH-AARP study and by pointing out some possible extensions of the current work.


Bayesian methods; Gibbs sampling; Measurement error models; Nonparametric regression; Truncated normals

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