文献类型：期刊文献

英文题名：On estimation of multivariate prediction regions in partial least squares regression

作者：Lin, Weilu[1];Zhuang, Yingping[1];Zhang, Siliang[1];Martin, Elaine[2]

机构：[1]East China Univ Sci Technol, Sate Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[2]Newcastle Univ, Sch Chem Engn & Adv Mat, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

年份：2013

卷号：27

期号：9

起止页码：243

外文期刊名：JOURNAL OF CHEMOMETRICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000324917000003)】；

基金：The authors thank the Initiative Fund for Young Researchers of ECUST (YF0157126) for the financial support.

语种：英文

外文关键词：multivariate prediction region; partial least squares; Jacobian matrix; local linearization

摘要：The estimation of the prediction region of partial least squares (PLS) is necessary in many engineering applications. However, research in this area focuses on the estimation of prediction intervals only. In this work, a new recursive formulation of PLS is proposed to facilitate the calculation of the Jacobian matrix of the estimated coefficient matrix. Furthermore, the computational complexity analysis indicates that the proposed algorithm is O(m(2)N+mpN+mpN(2)+mN(3)+mpN(4)) per number of component. The prediction region of the multivariate PLS is obtained through local linearization. The new formulation provides one way to obtain the prediction region of the multivariate PLS. Simulation and near-infrared spectra of corn case studies indicate the utility of the proposed method. Copyright (c) 2013 John Wiley & Sons, Ltd.