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December 2012 The transfer principle: A tool for complete case analysis
Hira L. Koul, Ursula U. Müller, Anton Schick
Ann. Statist. 40(6): 3031-3049 (December 2012). DOI: 10.1214/12-AOS1061

Abstract

This paper gives a general method for deriving limiting distributions of complete case statistics for missing data models from corresponding results for the model where all data are observed. This provides a convenient tool for obtaining the asymptotic behavior of complete case versions of established full data methods without lengthy proofs.

The methodology is illustrated by analyzing three inference procedures for partially linear regression models with responses missing at random. We first show that complete case versions of asymptotically efficient estimators of the slope parameter for the full model are efficient, thereby solving the problem of constructing efficient estimators of the slope parameter for this model. Second, we derive an asymptotically distribution free test for fitting a normal distribution to the errors. Finally, we obtain an asymptotically distribution free test for linearity, that is, for testing that the nonparametric component of these models is a constant. This test is new both when data are fully observed and when data are missing at random.

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Hira L. Koul. Ursula U. Müller. Anton Schick. "The transfer principle: A tool for complete case analysis." Ann. Statist. 40 (6) 3031 - 3049, December 2012. https://doi.org/10.1214/12-AOS1061

Information

Published: December 2012
First available in Project Euclid: 8 February 2013

zbMATH: 1296.62040
MathSciNet: MR3097968
Digital Object Identifier: 10.1214/12-AOS1061

Subjects:
Primary: 62E20
Secondary: 62G05 , 62G10

Keywords: efficient estimation , martingale transform test for normal errors , missing at random , partially linear models , testing for linearity , Transfer principle

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.40 • No. 6 • December 2012
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