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October 2001 Nearest neighbor classification with dependent training sequences
M. Holst, A. Irle
Ann. Statist. 29(5): 1424-1442 (October 2001). DOI: 10.1214/aos/1013203460

Abstract

The asymptotic classification risk for nearest neighbor procedures is well understood in the case of i.i.d. training sequences. In this article, we generalize these results to a class of dependent models including hidden Markov models. In the case where the observed patterns have Lebesgue densities, the asymptotic risk takes the same expression as in the i.i.d. case. For discrete distributions, we show that the asymptotic risk depends on the rule used for breaking ties of equal distances.

Citation

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M. Holst. A. Irle. "Nearest neighbor classification with dependent training sequences." Ann. Statist. 29 (5) 1424 - 1442, October 2001. https://doi.org/10.1214/aos/1013203460

Information

Published: October 2001
First available in Project Euclid: 8 February 2002

zbMATH: 1043.62057
MathSciNet: MR1873337
Digital Object Identifier: 10.1214/aos/1013203460

Subjects:
Primary: 62H30
Secondary: 62G20

Rights: Copyright © 2001 Institute of Mathematical Statistics

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Vol.29 • No. 5 • October 2001
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