Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models

By Snigdhansu Chatterjee, Partha Lahiri, Huilin Li. 2008

129 citations recorded by the source corpus.

Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems. The variability of an EBLUP is traditionally measured by the mean squared prediction error (MSPE), and interval estimates are generally constructed using estimates of the MSPE. Such methods have shortcomings like under-coverage or over-coverage, excessive length and lack of interpretability. We propose a parametric bootstrap approach to estimate the entire distribution of a suitably centered and scaled EBLUP. The bootstrap histogram is highly accurate, and differs from the true EBLUP distribution by only O(d 3 n -3/2 ), where d is the number of parameters and n the number of observations. This result is used to obtain highly accurate prediction intervals. Simulation results demonstrate the superiority of this method over existing techniques of constructing prediction intervals in linear mixed models.

Subfields: Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6332bb86736a80be07749111277724d4516654aa

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In Environmental Engineering

In Statistics and Probability

Citations in the corpus

Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data (2011, background): “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).”