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August 2006 Nonparametric estimation of mean-squared prediction error in nested-error regression models
Peter Hall, Tapabrata Maiti
Ann. Statist. 34(4): 1733-1750 (August 2006). DOI: 10.1214/009053606000000579


Nested-error regression models are widely used for analyzing clustered data. For example, they are often applied to two-stage sample surveys, and in biology and econometrics. Prediction is usually the main goal of such analyses, and mean-squared prediction error is the main way in which prediction performance is measured. In this paper we suggest a new approach to estimating mean-squared prediction error. We introduce a matched-moment, double-bootstrap algorithm, enabling the notorious underestimation of the naive mean-squared error estimator to be substantially reduced. Our approach does not require specific assumptions about the distributions of errors. Additionally, it is simple and easy to apply. This is achieved through using Monte Carlo simulation to implicitly develop formulae which, in a more conventional approach, would be derived laboriously by mathematical arguments.


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Peter Hall. Tapabrata Maiti. "Nonparametric estimation of mean-squared prediction error in nested-error regression models." Ann. Statist. 34 (4) 1733 - 1750, August 2006.


Published: August 2006
First available in Project Euclid: 3 November 2006

zbMATH: 1246.62106
MathSciNet: MR2283715
Digital Object Identifier: 10.1214/009053606000000579

Primary: 62F12, 62J99

Rights: Copyright © 2006 Institute of Mathematical Statistics


Vol.34 • No. 4 • August 2006
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