Nonparametric estimation of mean-squared prediction error in nested-error regression models

By Peter Hall, Tapabrata Maiti. 2005

105 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4992a7b9a13dcedb50f839aaffc0af34da2bcc65

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Citations in the corpus

Bootstrap mean squared error of a small-area EBLUP (2008, methodology): “Recently, Hall and Maiti [18, 19] introduced a parametric and a matched-moment double-bootstrap algorithms, and González-Manteiga et al. [20] applied a wild bootstrap procedure to logistic mixed models.”
Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data (2011, methodology): “See, for example, the work of Battese, Harter, and Fuller (1988), Prasad and Rao (1990), Datta and Ghosh (1991), Singh, Stukel, and Pfeffermann (1998), Datta and Lahiri (2000), Hall and Maiti (2006a, 2006b), and Chatterjee, Lahiri, and Li (2008).”
Best Predictive Small Area Estimation (2011, methodology): “Similar approaches have been used, for example, by Hall and Maiti (2006), to deal with the situation where their nonparametric bootstrap MSPE estimator is negative.”