Partial least squares for dependent data.
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.
Institute for Mathematical Stochastics, Georg-August-Universität Göttingen, Goldschmidtstr. 7, 37077 Göttingen, Germany , tkrivob@uni-goettingen.de munk@math.uni-goettingen.de.
Marco
M
Singer
Institute for Mathematical Stochastics, Georg-August-Universität Göttingen, Goldschmidtstr. 7, 37077 Göttingen, Germany , tkrivob@uni-goettingen.de munk@math.uni-goettingen.de.
Tatyana
T
Krivobokova
Institute for Mathematical Stochastics, Georg-August-Universität Göttingen, Goldschmidtstr. 7, 37077 Göttingen, Germany , tkrivob@uni-goettingen.de munk@math.uni-goettingen.de.
Axel
A
Munk
Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany , bgroot@gwdg.de.
Bert
B
de Groot
Dependent data; Latent variable model; Nonstationary process; Partial least squares; Protein dynamics