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An Alternative Choice of Smoothing for Kernel-Based Density Estimates in Discrete Discriminant Analysis
Vol. 73, No. 2 (Aug., 1986), pp. 405-411
Stable URL: http://www.jstor.org/stable/2336217
Page Count: 7
You can always find the topics here!Topics: Density estimation, Estimators, Data smoothing, Estimation methods, Discriminant analysis, Consistent estimators, Analytical estimating, Mathematical independent variables, Population estimates, Mathematical vectors
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The kernel method of estimating the cell probabilities of a multivariate categorical distribution, due to Aitchison & Aitken (1976), depends crucially on an unknown smoothing parameter λ. A method of estimating λ is introduced which is explicitly connected to multivariate discrimination. The method, based on maximization of the leaving-one-out estimator of the nonerror rate, is shown to be Bayes risk strongly consistent. An example is given to illustrate the application.
Biometrika © 1986 Biometrika Trust