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Procedures for Reduced‐Rank Regression

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  • P. T. Davies
  • M. K‐S. Tso

Abstract

We discuss in this paper procedures for the analysis of the reduced‐rank regression model. A new method is proposed for parameter estimation which is justified by a least‐squares analysis employing matrix singular‐value decomposition and the Eckart‐Young theorem. The application of the model is illustrated by the regression analysis of gasoline distillation measurements on composition data obtained by gas‐liquid chromatography.

Suggested Citation

  • P. T. Davies & M. K‐S. Tso, 1982. "Procedures for Reduced‐Rank Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 244-255, November.
    • Handle: RePEc:bla:jorssc:v:31:y:1982:i:3:p:244-255
    DOI: 10.2307/2347998
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    Cited by:

    1. Verniest, Fabien & Greulich, Sabine, 2019. "Methods for assessing the effects of environmental parameters on biological communities in long-term ecological studies - A literature review," Ecological Modelling, Elsevier, vol. 414(C).
    2. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.

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