Robust Box-Cox transformations based on minimum residual autocorrelation

Details

Serval ID
serval:BIB_142E3A3B0722
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Robust Box-Cox transformations based on minimum residual autocorrelation
Journal
Computational Statistics and Data Analysis
Author(s)
Marazzi Alfio, Yohai Victor J.
ISSN
0167-9473
Publication state
Published
Issued date
2006
Volume
50
Number
10
Pages
2752-2768
Language
english
Notes
SAPHIRID:47921
Abstract
Response transformations are a popular approach to adapt data to a linear regression model. The regression coefficients, as well as the parameter defining the transformation, are often estimated by maximum likelihood assuming homoscedastic normal errors. Unfortunately, consistency to the true parameters holds only if the assumptions of normality and homoscedasticity are satisfied. In addition, these estimates are nonrobust in the presence of outliers. New estimates are proposed, which are robust and consistent even if the assumptions of normality and homoscedasticity do not hold. These estimates are based on the minimization of a robust measure of residual autocorrelation. [Authors]
Keywords
Box-Cox transformation, heteroscedasticity, robust estimation, regression
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Create date
04/03/2008 14:58
Last modification date
20/08/2019 12:42
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