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May, 1973 The Choice of Variables for Prediction in Curvilinear Multiple Regression
R. J. Brooks
Ann. Statist. 1(3): 506-516 (May, 1973). DOI: 10.1214/aos/1176342416

Abstract

A Bayesian formulation of the problem of analysing data from a curvilinear regression of $y$ on $x_1, x_2, \cdots, x_r$ in order to predict a future value of $y$ is considered. The problem is to obtain a criterion to decide which is the best subset of $x_1, x_2, \cdots, x_r$ to perform this prediction. Under very strict assumptions the criterion obtained is shown to use the same statistic as the orthodox (least squares) approach.

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R. J. Brooks. "The Choice of Variables for Prediction in Curvilinear Multiple Regression." Ann. Statist. 1 (3) 506 - 516, May, 1973. https://doi.org/10.1214/aos/1176342416

Information

Published: May, 1973
First available in Project Euclid: 12 April 2007

zbMATH: 0271.62076
MathSciNet: MR353579
Digital Object Identifier: 10.1214/aos/1176342416

Rights: Copyright © 1973 Institute of Mathematical Statistics

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Vol.1 • No. 3 • May, 1973
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