Bayesian Smoothing for Measurement Error Problems

Scott M. Berry (), Raymond J. Carroll () and David Ruppert ()
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Scott M. Berry: 1039 Wellington Court Sycamore, Berry Consultants
Raymond J. Carroll: Texas A&M University, Department of Statistics
David Ruppert: Cornell University, School of Operations Research and Industrial Engineering

A chapter in Total Least Squares and Errors-in-Variables Modeling, 2002, pp 121-130 from Springer

Abstract: Abstract In the presence of covariate measurement error, estimating a regression function nonparametrically is extremely difficult, the problem being related to deconvolution. In this paper we describe Bayesian approaches to modeling a flexible regression function when the predictor variable is measured with error. The regression function is modeled with smoothing splines.. We provide simulations with several nonlinear regression functions. Our simulations indicate that the frequentist mean squared error properties of the fully Bayesian method are better than those of previously proposed frequentist methods, at least in the examples we have studied.

Keywords: Bayesian methods; errors-in-variables model; functional relationship; generalized linear models; kernel regression; measurement error; nonparametric methods; SIMEX; splines; structural relationship. (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-3552-0_11

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DOI: 10.1007/978-94-017-3552-0_11

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