Box-Cox symmetric distributions and applications to nutritional data

TL;DR

This paper introduces the Box-Cox symmetric distribution class, offering flexible modeling for skewed and heavy-tailed data, with applications demonstrated in nutritional data analysis.

Contribution

It defines a new flexible distribution class that encompasses several existing distributions, facilitating regression modeling and outlier handling.

Findings

Effective modeling of skewed nutritional data

Inclusion of multiple distributions as special cases

Enhanced outlier robustness in data analysis

Abstract

We introduce the Box-Cox symmetric class of distributions, which is useful for modeling positively skewed, possibly heavy-tailed, data. The new class of distributions includes the Box-Cox t, Box-Cox Cole-Gree, Box-Cox power exponential distributions, and the class of the log-symmetric distributions as special cases. It provides easy parameter interpretation, which makes it convenient for regression modeling purposes. Additionally, it provides enough flexibility to handle outliers. The usefulness of the Box-Cox symmetric models is illustrated in applications to nutritional data.