Partial least squares and compositional data: Problems and alternatives

被引：26
|
作者：
Hinkle, J
Rayens, W
机构：
[1] Department of Statistics, University of Kentucky, Lexington
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1995年 / 30卷 / 01期
基金：
美国国家科学基金会;
关键词：
partial least squares; compositional data;
D O I：
10.1016/0169-7439(95)00062-3
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
It is still widely unknown in chemometrics that the statistical analysis of compositional data requires fundamentally different tools than a similar analysis of unconstrained data. This article examines the problems that potentially occur when one performs a partial least squares (PLS) analysis on compositional data and suggests logcontrast partial least squares (LCPLS) as an alternative.
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页码：159 / 172
页数：14
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