JSM 2004 - Toronto

Abstract #300887

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Activity Number: 376
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #300887
Title: Robust Transformation for Linear Regression with Both Continuous and Binary Regressors
Author(s): Tsung-Chi Cheng*+
Companies: National Chengchi University
Address: 64 Chih-Nan Rd., Section 2, Taipei, 11623, Taiwan
Keywords: Box-Cox transformation ; regression diagnostics ; robust estimator
Abstract:

The problem of non-normality may often be simplified by an appropriate transformation, such as the parametric family of power transformations in Box and Cox (1964). However, the evidence for transformations may sometimes depend crucially on one or a few observations. Several authors have pointed out that data transformations are very sensitive to outliers. A robust estimate of the transformation parameter is proposed for the cases when both continuous and binary regressors exist in the linear regression model. The proposed procedure extends the ideas of Hubert and Rousseeuw (1997), in which the robust distance of the continuous regressors is used to be the weight for L1 regression. The purpose of the present article intends to avoid the influence from potential outliers during the process of data transformations. Simulation study and real data analysis show the performance of the resulting approach.

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