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The general Box-Cox transformations in multiple linear regression analysis

Lookup NU author(s): Baibaing Li

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Abstract

A general Box-Cox transformation method in multiple linear regressions is investigated. An algorithm. is proposed to identify optimal general Box-Cox transformations based. on kernel density estimation techniques. It is shown that for a, multiple linear regression problem, the optimal general Box-Cox transformation can be derived through solving a matrix eigenvector problem, while the regression coefficients are estimated by least squares approach. Examples are given to illustrate the proposed method.

Publication metadata

Author(s): Li BB, De Moor B

Publication type: Article

Publication status: Published

Journal: Communications in Statistics: Simulation and Computation

Year: 2002

Volume: 31

Issue: 4

Pages: 673-687

Print publication date: 01/01/2002

ISSN (print): 0361-0918

ISSN (electronic): 1532-4141

Publisher: Taylor & Francis Inc.

URL: http://dx.doi.org/10.1081/SAC-120004319

DOI: 10.1081/SAC-120004319

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