Canonical Variate Analysis of High-Dimensional Spectral Data
作者:
H.T. Kiiveri,
期刊:
Technometrics
(Taylor Available online 1992)
卷期:
Volume 34,
issue 3
页码: 321-331
ISSN:0040-1706
年代: 1992
DOI:10.1080/00401706.1992.10485281
出版商: Taylor & Francis Group
关键词: Bayesian information criterion;EM algorithm; Factor analysis;Generalized eigenvalue problem;Growth-curve model;Integral equation
数据来源: Taylor
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.
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