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May, 1978 Asymptotically Efficient Solutions to the Classification Problem
Louis Gordon, Richard A. Olshen
Ann. Statist. 6(3): 515-533 (May, 1978). DOI: 10.1214/aos/1176344197

Abstract

We study a class of decision rules based on an adaptive partitioning of an Euclidean observation space. The class of partitions has a computationally attractive form, and the related decision rule is invariant under strictly monotone transformations of coordinate axes. We provide sufficient conditions that a sequence of decision rules be asymptotically Bayes risk efficient as sample size increases. The sufficient conditions involve no regularity assumptions on the underlying parent distributions.

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Louis Gordon. Richard A. Olshen. "Asymptotically Efficient Solutions to the Classification Problem." Ann. Statist. 6 (3) 515 - 533, May, 1978. https://doi.org/10.1214/aos/1176344197

Information

Published: May, 1978
First available in Project Euclid: 12 April 2007

zbMATH: 0437.62056
MathSciNet: MR468035
Digital Object Identifier: 10.1214/aos/1176344197

Subjects:
Primary: 62H30
Secondary: 62G20, 62P99

Rights: Copyright © 1978 Institute of Mathematical Statistics

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Vol.6 • No. 3 • May, 1978
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