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Generalized Semiparametric Regression with Covariates Measured with Error

In: Statistical Modelling and Regression Structures

Author

Listed:
  • Thomas Kneib

    (Carl von Ossietzky Universität Oldenburg, Institut für Mathematik)

  • Andreas Brezger

    (HypoVereinsbank Munich)

  • Ciprian M. Crainiceanu

    (Johns-Hopkins-University Baltimore, Department of Biostatistics)

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.

Suggested Citation

  • Thomas Kneib & Andreas Brezger & Ciprian M. Crainiceanu, 2010. "Generalized Semiparametric Regression with Covariates Measured with Error," Springer Books, in: Thomas Kneib & Gerhard Tutz (ed.), Statistical Modelling and Regression Structures, pages 133-154, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2413-1_8
    DOI: 10.1007/978-3-7908-2413-1_8
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