Discrimination with Many Variables
Discrimination with Many Variables
复制标题
多变量的歧视
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
M. Haque
中科院分区：
文献类型：
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作者：
P. Brown;T. Fearn;M. Haque
Abstract Many statistical methods for discriminant analysis do not adapt well or easily to situations where the number of variables is large, possibly even exceeding the number of cases in the training set. We explore a variety of methods for providing robust identification of future samples in this situation. We develop a range of flexible Bayesian methods, and primarily a new hierarchical covariance compromise method, akin to regularized discriminant analysis. Although the methods are much more widely applicable, the motivating problem was that of discriminating between groups of samples on the basis of their near-infrared spectra. Here the ability of the Bayesian methods to take account of continuity of the spectra may be beneficial. The spectra may consist of absorbances or reflectances at as many as 1,000 wavelengths, and yet there may be only tens or hundreds of training samples in which both sample spectrum and group identity are known. Such problems arise in the food and pharmaceutical industries;...
