Approximate repeated-measures shrinkage

Adam R. Brentnall, Martin J. Crowder and David J. Hand

Computational Statistics & Data Analysis, 2011, vol. 55, issue 2, 1150-1159

Abstract: A general method is formalised for the problem of making predictions for a fixed group of individual units, following a sequence of repeated measures on each. A review of some related work is undertaken and, using some of its terminology, the approach might be described as approximate non-parametric empirical Bayes prediction. It is contended that the method may often produce predictions that are, in practice, comparable or not much worse than more sophisticated methods, but sometimes for a smaller computational cost. Two examples are used to demonstrate the approach, exploring the prediction of baseball averages and spatial-temporal rainfall. The method performs favourably in both examples in comparison with James-Stein, empirical Bayes and other predictions; it also provides a relatively simple and computationally feasible way of determining whether it is worth modelling between-individual variability.

Keywords: Empirical; Bayes; Prediction; Random; effects (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00357-9
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:2:p:1150-1159

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().