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Empirical Bayes in-season prediction of baseball batting averages

Wenhua Jiang and Cun-Hui Zhang

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Abstract

The performance of a number of empirical Bayes methods are examined for the in-season prediction of batting averages with the 2005 Major League baseball data. Among the methodologies considered are new general empirical Bayes estimators in homoscedastic and heteroscedastic partial linear models.

Chapter information

Source
James O. Berger, T. Tony Cai and Iain M. Johnstone, eds., Borrowing Strength: Theory Powering Applications – A Festschrift for Lawrence D. Brown (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2010), 263-273

Dates
First available in Project Euclid: 26 October 2010

Permanent link to this document
http://projecteuclid.org/euclid.imsc/1288099025

Digital Object Identifier
doi:10.1214/10-IMSCOLL618

Subjects
Primary: 62J05: Linear regression 62J07: Ridge regression; shrinkage estimators
Secondary: 62H12: Estimation 62H25: Factor analysis and principal components; correspondence analysis

Keywords
empirical Bayes compound decisions partial linear model nonparametric estimation semiparametric estimation sports hitting batting average

Rights
Copyright © 2010, Institute of Mathematical Statistics

Citation

Jiang, Wenhua; Zhang, Cun-Hui. Empirical Bayes in-season prediction of baseball batting averages. Borrowing Strength: Theory Powering Applications – A Festschrift for Lawrence D. Brown, 263--273, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2010. doi:10.1214/10-IMSCOLL618. http://projecteuclid.org/euclid.imsc/1288099025.


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References

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