ACE, AVAS and Robust Data Transformations

Unlike the Box-Cox transformation, that of Yeo and Johnson for the response of a linear model can be applied when the observations are not constrained to be positive. We study the extended Yeo–Johnson transformation in which positive and negative observations can be transformed with different parameter values. The procedure is illustrated for data with many outliers. The data are cleaned with a robust method, the forward search, and the obtained transformations compared with the results from two nonparametric transformation methods based on data smoothing.