Partial least squares for dependent data.

Singer, M.; Krivobokova, T.; Munk, A.; De Groot, B. L.

doi:10.1093/biomet/asw010

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Journal Article

Partial least squares for dependent data.

MPS-Authors

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Munk, A.
Research Group of Statistical Inverse-Problems in Biophysics, MPI for biophysical chemistry, Max Planck Society;

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De Groot, B. L.
Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society;

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http://biomet.oxfordjournals.org/content/103/2/351.full
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2307018_Suppl.pdf
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Citation

Singer, M., Krivobokova, T., Munk, A., & De Groot, B. L. (2016). Partial least squares for dependent data. Biometrika, 103(2), 351-362. doi: 10.1093/biomet/asw010.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-EDF7-D

Abstract

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.