Nonparametric regression in the presence of measurement error Academic Article

abstract

• In many regression applications the independent variable is measured with error. When this happens, conventional parametric and nonparametric regression techniques are no longer valid. We consider two different approaches to nonparametric regression. The first uses the SIMEX, simulation-extrapolation, method and makes no assumption about the distribution of the unobserved error-prone predictor. For this approach we derive an asymptotic theory for kernel regression which has some surprising implications. Penalised regression splines are also considered for fixed number of known knots. The second approach assumes that the error-prone predictor has a distribution of a mixture of normals with an unknown number of components, and uses regression splines. Simulations illustrate the results. 1999 Biometrika Trust.

authors

• Carroll, Raymond

published proceedings

• BIOMETRIKA

altmetric score

• 3

author list (cited authors)

• Carroll, R. J., Maca, J. D., & Ruppert, D.

citation count

• 125

complete list of authors

• Carroll, RJ||Maca, JD||Ruppert, D

publication date

• September 1999

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biometrika  Journal

keywords

• Estimating Equation
• Local Polynomial Regression
• Measurement Error
• Regression Spline
• Sandwich Estimation
• Simex

Digital Object Identifier (DOI)

• 10.1093/biomet/86.3.541

start page

• 541

end page

• 554

volume

• 86

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1093%2Fbiomet%2F86.3.541