The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special Case

I present a parametric, bijective transformation to generate heavy tail versions of arbitrary random variables. The tail behavior of this heavy tail Lambert  W × FX random variable depends on a tail parameter δ≥0: for δ=0, Y≡X, for δ>0 Y has heavier tails than X. For X being Gaussian it reduces t...

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Main Author: Georg M. Goerg
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2015/909231
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author Georg M. Goerg
author_facet Georg M. Goerg
author_sort Georg M. Goerg
collection DOAJ
description I present a parametric, bijective transformation to generate heavy tail versions of arbitrary random variables. The tail behavior of this heavy tail Lambert  W × FX random variable depends on a tail parameter δ≥0: for δ=0, Y≡X, for δ>0 Y has heavier tails than X. For X being Gaussian it reduces to Tukey’s h distribution. The Lambert W function provides an explicit inverse transformation, which can thus remove heavy tails from observed data. It also provides closed-form expressions for the cumulative distribution (cdf) and probability density function (pdf). As a special case, these yield analytic expression for Tukey’s h pdf and cdf. Parameters can be estimated by maximum likelihood and applications to S&P 500 log-returns demonstrate the usefulness of the presented methodology. The R package LambertW implements most of the introduced methodology and is publicly available on CRAN.
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spelling doaj-art-c50d7b5a7de54ba09e3a3fc8cfe3b8082025-08-20T03:23:04ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/909231909231The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special CaseGeorg M. Goerg0Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USAI present a parametric, bijective transformation to generate heavy tail versions of arbitrary random variables. The tail behavior of this heavy tail Lambert  W × FX random variable depends on a tail parameter δ≥0: for δ=0, Y≡X, for δ>0 Y has heavier tails than X. For X being Gaussian it reduces to Tukey’s h distribution. The Lambert W function provides an explicit inverse transformation, which can thus remove heavy tails from observed data. It also provides closed-form expressions for the cumulative distribution (cdf) and probability density function (pdf). As a special case, these yield analytic expression for Tukey’s h pdf and cdf. Parameters can be estimated by maximum likelihood and applications to S&P 500 log-returns demonstrate the usefulness of the presented methodology. The R package LambertW implements most of the introduced methodology and is publicly available on CRAN.http://dx.doi.org/10.1155/2015/909231
spellingShingle Georg M. Goerg
The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special Case
The Scientific World Journal
title The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special Case
title_full The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special Case
title_fullStr The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special Case
title_full_unstemmed The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special Case
title_short The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey’s h Transformation as a Special Case
title_sort lambert way to gaussianize heavy tailed data with the inverse of tukey s h transformation as a special case
url http://dx.doi.org/10.1155/2015/909231
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