CANONICAL VARIATE ANALYSIS OF HIGH-DIMENSIONAL SPECTRAL DATA

被引:10
|
作者
KIIVERI, HT
机构
关键词
BAYESIAN INFORMATION CRITERION; EM ALGORITHM; FACTOR ANALYSIS; GENERALIZED EIGENVALUE PROBLEM; GROWTH-CURVE MODEL; INTEGRAL EQUATION;
D O I
10.2307/1270038
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
This article is concerned with quantifying and representing group differences when there are more variables than observations. In particular, canonical variate analysis when the data consist of curves sampled at many grid points is considered. A new method is proposed that involves replacing the usually singular within-groups variation matrix by a fitted matrix that is positive-definite. To obtain the fitted matrix, a class of models, along with associated estimation and model-selection procedures, is presented. The results are applied to experimental data designed to assess the usefulness of data from a portable field spectrometer for discriminating between usable farmland and farmland affected by salinity.
引用
收藏
页码:321 / 331
页数:11
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