Dual‐ and triple‐mode matrix approximation and regression modelling | Zendy

Lipovetsky Stan | Zendy; Michael Conklin W. | Zendy

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Dual‐ and triple‐mode matrix approximation and regression modelling

Author(s) -

Lipovetsky Stan,

Michael Conklin W.

Publication year - 2003

Publication title -

applied stochastic models in business and industry

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.413

H-Index - 40

eISSN - 1526-4025

pISSN - 1524-1904

DOI - 10.1002/asmb.503

Subject(s) - singular value decomposition , interpretation (philosophy) , regression , mathematics , regression analysis , matrix (chemical analysis) , robust regression , total least squares , dual (grammatical number) , set (abstract data type) , principal component regression , computer science , statistics , algorithm , art , materials science , literature , composite material , programming language

We propose a dual‐ and triple‐mode least squares for matrix approximation. This technique applied to the singular value decomposition produces the classical solution with a new interpretation. Applied to regression modelling, this approach corresponds to a regularized objective and yields a new solution with properties of a ridge regression. The results for regression are robust and suggest a convenient tool for the analysis and interpretation of the model coefficients. Numerical results are given for a marketing research data set. Copyright © 2003 John Wiley & Sons, Ltd.

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