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  Partial least squares for dependent data.

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-EDF7-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-1D5C-E
Genre: Journal Article

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 Creators:
Singer, M., Author
Krivobokova, T., Author
Munk, A.1, Author              
De Groot, B. L.2, Author              
Affiliations:
1Research Group of Statistical Inverse-Problems in Biophysics, MPI for biophysical chemistry, Max Planck Society, ou_1113580              
2Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society, ou_578573              

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Free keywords: Dependent data; Latent variable model; Nonstationary process; Partial least squares; Protein dynamics
 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.

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Language(s): eng - English
 Dates: 2016-04-292016-06
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1093/biomet/asw010
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Title: Biometrika
Source Genre: Journal
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Pages: - Volume / Issue: 103 (2) Sequence Number: - Start / End Page: 351 - 362 Identifier: -