Generalized Semiparametric Regression with Covariates Measured with Error

Thomas Kneib (), Andreas Brezger () and Ciprian M. Crainiceanu ()
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Thomas Kneib: Carl von Ossietzky Universität Oldenburg, Institut für Mathematik
Andreas Brezger: HypoVereinsbank Munich
Ciprian M. Crainiceanu: Johns-Hopkins-University Baltimore, Department of Biostatistics

A chapter in Statistical Modelling and Regression Structures, 2010, pp 133-154 from Springer

Abstract: Abstract We develop generalized semiparametric regression models for exponential family and hazard regression where multiple covariates are measured with error and the functional form of their effects remains unspecified. The main building blocks in our approach are Bayesian penalized splines and Markov chain Monte Carlo simulation techniques. These enable a modular and numerically efficient implementation of Bayesian measurement error correction based on the imputation of true, unobserved covariate values. We investigate the performance of the proposed correction in simulations and an epidemiological study where the duration time to detection of heart failure is related to kidney function and systolic blood pressure.

Keywords: additive hazard regression; generalized additive models; MCMC; measurement error correction; penalized splines (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2413-1_8

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DOI: 10.1007/978-3-7908-2413-1_8

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