A Robust Method for Testing Transformations to Achieve Approximate Normality

Abstract

Summary We propose a competitor to likelihood and significance methods for power transformations to achieve approximate normality in a linear model. The new method is shown in theory and a Monte Carlo experiment to produce more robust inferences than the likelihood method and more powerful (although possibly slightly less robust) inferences than the significance method.