Geometric Classifier for Multiclass, High-Dimensional Data
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In this article, we consider a geometric classifier that is applicable to multiclass classification for high-dimensional data. We show the consistency property and the asymptotic normality of the geometric classifier under certain mild conditions. We discuss sample size determination so that the geometric classifier can ensure that its misclassification rates are less than prespecified thresholds. We give a two-stage procedure to estimate the sample sizes required in such a geometric classifier and propose a misclassification rate–adjusted classifier (MRAC) based on the geometric classifier. We evaluate the performance of the MRAC theoretically and numerically. Finally, we demonstrate the MRAC in actual data analyses by using a microarray data set.
- Sequential analysis
Sequential analysis 34(3), 279-294
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