ANALYSIS OF MIXTURE DATA WITH PARTIAL LEAST-SQUARES
ANALYSIS OF MIXTURE DATA WITH PARTIAL LEAST-SQUARES
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DOI：
10.1016/0169-7439(92)80092-i
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发表时间：
1992-04-01
影响因子：
3.9
通讯作者：
KETTANEHWOLD, N
中科院分区：
文献类型：
--
作者：
KETTANEHWOLD, N
混合数据的分析是工业研究和开发中的一个常见问题，特别是在化学和相关行业，如制药、化妆品、石油和生物技术。由于混合约束，用多元回归分析混合数据需要特殊的模型形式。将讨论Scheffe和Cox的典型多项式，以及约束区域数据的多元回归的局限性。对于混合数据的分析，偏最小二乘（PLS）是实用的。特别是当涉及混合变量和过程变量时，它提供了一种灵活而简单的方法，在实践中效果良好。混合数据的分析使用PLS和多元回归进行比较，从科学文献的案例研究。
The analysis of mixture data is a common problem in industrial research and development, particularly in chemical and related industries, e.g. pharmaceuticals, cosmetics, oil, and biotechnology. Analyzing mixture data with multiple regression necessitates special model forms due to the mixture constraint. The canonical polynomials of Scheffe and of Cox will be discussed, as well as the limitation of multiple regression with data in constrained regions. For the analysis of mixture data, partial least squares (PLS) has been found to be practical. In particular when both mixture and process variables are involved, it offers a flexible and simple approach which works well in practice. The analysis of mixture data using PLS and multiple regression are compared, with case studies from the scientific literature.