A quantile approach to the box-cox transformation in random samples.

Abstract

This paper presents an alternative approach to the likelihood methods for estimating the parameter A in the Box-Cox family of transformations when the data arise from a random sample. The method is based on a representation of the quantile function of the variable under consideration. Theoretical properties of the method, its practical applications and comparison with the likelihood approach are studied.Asymptotic relative efficiency (ARE); Box-Cox transformation; Influential observations; Quantile function; Kernel density estimation;