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Partial least squares for dependent data

Author

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  • Marco Singer
  • Tatyana Krivobokova
  • Axel Munk
  • Bert de Groot

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.

Suggested Citation

  • Marco Singer & Tatyana Krivobokova & Axel Munk & Bert de Groot, 2016. "Partial least squares for dependent data," Biometrika, Biometrika Trust, vol. 103(2), pages 351-362.
    • Handle: RePEc:oup:biomet:v:103:y:2016:i:2:p:351-362.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw010
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    References listed on IDEAS

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    1. Carsten Hahn & Michael D. Johnson & Andreas Herrmann & Frank Huber, 2002. "Capturing Customer Heterogeneity Using A Finite Mixture Pls Approach," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 243-269, July.
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    Cited by:

    1. Zhou, Zhiyang, 2021. "Fast implementation of partial least squares for function-on-function regression," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    2. Ekvall, Karl Oskar, 2022. "Targeted principal components regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    3. Zhun Cheng & Zhixiong Lu, 2021. "Research on Load Disturbance Based Variable Speed PID Control and a Novel Denoising Method Based Effect Evaluation of HST for Agricultural Machinery," Agriculture, MDPI, vol. 11(10), pages 1-18, October.

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