  Modern data analysis frequently involves variables with highly non-Gaussian
marginal distributions. However, commonly used analysis methods are most
effective with roughly Gaussian data. This paper introduces an automatic
transformation that improves the closeness of distributions to normality. For
each variable, a new family of parametrizations of the shifted logarithm
transformation is proposed, which is unique in treating the data as
real-valued, and in allowing transformation for both left and right skewness
within the single family. This also allows an automatic selection of the
parameter value (which is crucial for high dimensional data with many variables
to transform) by minimizing the Anderson-Darling test statistic of the
transformed data. An application to image features extracted from melanoma
microscopy slides demonstrate the utility of the proposed transformation in
addressing data with excessive skewness, heteroscedasticity and influential
observations.
