Prediction based on mean subset

Ojelund, H.. and Brown, P.J. and Madsen, H. and Thyregod, P. (2002) Prediction based on mean subset. Technometrics, 44 (4). pp. 369-374. ISSN 0040-1706. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1198/004017002188618563

Abstract

Shrinkage methods have traditionally been applied in prediction problems. In this article we develop a shrinkage method (mean subset) that forms an average of regression coefficients from individual subsets of the explanatory variables. A Bayesian approach is taken to derive an expression of how the coefficient vectors from each subset should be weighted. It is not computationally feasible to calculate the mean subset coefficient vector for larger problems, and thus we suggest an algorithm to find an approximation to the mean subset coefficient vector. In a comprehensive Monte Carlo simulation study, it is found that the proposed mean subset method has superior prediction performance than prediction based on the best subset method, and in some settings also better than the ridge regression and lasso methods. The conclusions drawn from the Monte Carlo study is corroborated in an example in which prediction is made using spectroscopic data.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science
Depositing User: Philip J Brown
Date Deposited: 09 Oct 2008 17:38
Last Modified: 14 Jan 2010 14:29
Resource URI: http://kar.kent.ac.uk/id/eprint/8142 (The current URI for this page, for reference purposes)
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