Partial least squares for dependent data

被引：14

作者：

Singer, Marco ^{[1
]}

Krivobokova, Tatyana ^{[1
]}

Munk, Axel ^{[1
]}

De Groot, Bert ^{[2
]}

机构：

[1] Univ Gottingen, Inst Math Stochast, Goldschmidtstr 7, D-37077 Gottingen, Germany

[2] Max Planck Inst Biophys Chem, Fassberg 11, D-37077 Gottingen, Germany

来源：

BIOMETRIKA | 2016年 / 103卷 / 02期

关键词：

Dependent data; Latent variable model; Nonstationary process; Partial least squares; Protein dynamics; REGRESSION; PROTEINS; PLS; CHEMOMETRICS; PREDICTION; MATRICES; MOTIONS; MODELS;

D O I：

10.1093/biomet/asw010

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We consider the partial least squares algorithm for dependent data and study the consequences of ignoring the dependence both theoretically and numerically. Ignoring nonstationary dependence structures can lead to inconsistent estimation, but a simple modification yields consistent estimation. A protein dynamics example illustrates the superior predictive power of the proposed method.

引用

页码：351 / 362

页数：12

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