Nonparametric variance estimation in the analysis of microarray data: a measurement error approach. Academic Article

abstract

• This article investigates the effects of measurement error on the estimation of nonparametric variance functions. We show that either ignoring measurement error or direct application of the simulation extrapolation, SIMEX, method leads to inconsistent estimators. Nevertheless, the direct SIMEX method can reduce bias relative to a naive estimator. We further propose a permutation SIMEX method which leads to consistent estimators in theory. The performance of both SIMEX methods depends on approximations to the exact extrapolants. Simulations show that both SIMEX methods perform better than ignoring measurement error. The methodology is illustrated using microarray data from colon cancer patients.

authors

• Carroll, Raymond

published proceedings

• Biometrika

author list (cited authors)

• Carroll, R. J., & Wang, Y.

citation count

• 17

complete list of authors

• Carroll, Raymond J||Wang, Yuedong

publication date

• June 2008

publisher

• Oxford University Press (OUP)  Publisher

keywords

• Heteroscedasticity
• Local Polynomial Regression
• Measurement Error
• Microarray
• Nonparametric Regression
• Permutation
• Simex
• Simulation-extrapolation
• Variance Function Estimation

Digital Object Identifier (DOI)

• 10.1093/biomet/asn017

start page

• 437

end page

• 449

volume

• 95

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1093%2Fbiomet%2Fasn017