Nonparametric estimation of mean-squared prediction error in nested-error regression models
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Annals of Statistics 2006, Vol. 34, No. 4, 1733-1750|
|Access Rights:||Open Access|
|01_Peter Hal_Nonparametric_estimation_of_2005.pdf||Published Version||255.94 kB||Adobe PDF|
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