Journal of the Japanese Society of Computational Statistics
Online ISSN : 1881-1337
Print ISSN : 0915-2350
ISSN-L : 0915-2350
Theory and Applications
Box and Cox power-transformation to additivity and homoscedasticity in regression
Toshimitsu HamasakiTomoyuki SugimotoMasashi Goto
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2010 年 23 巻 1 号 p. 1_13-1_26

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We describe a Box and Cox power-transformation to simultaneously provide additivity and homoscedasticity in regression. The two methods developed here are extensions of the power-additive transformation (PAT) discussed by Goto (1992, 1995) and Hamasaki and Goto (2005). The PAT aims to improve the additivity or linearity of some simple model represented by linear predicators. We then consider combinations of the PAT with the weighting and transform-both-sides methods. We discuss the procedures to find the maximum likelihood estimates of parameters and then consider the relationship between the methods. Also, we compare the performances of the methods through a simulation study.
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