Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification

© 2016 by the Mathematical Association of Thailand. All rights reserved. A large number of efficient statistical methods have been designed for a frequent case when the distributions are normal (Gaussian). In practice, many probability distributions are not normal. In this case, Gaussian-based techn...

Full description

Saved in:

Bibliographic Details
Main Authors:	Dumrongpokaphan T., Barragan P., Kreinovich V.
Format:	Journal
Published:	2017
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008395342&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42451
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Chiang Mai University

id	th-cmuir.6653943832-42451
record_format	dspace
spelling	th-cmuir.6653943832-424512017-09-28T04:27:12Z Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification Dumrongpokaphan T. Barragan P. Kreinovich V. © 2016 by the Mathematical Association of Thailand. All rights reserved. A large number of efficient statistical methods have been designed for a frequent case when the distributions are normal (Gaussian). In practice, many probability distributions are not normal. In this case, Gaussian-based techniques cannot be directly applied. In many cases, however, we can apply these techniques indirectly – by first applying an appropriate transformation to the original variables, after which their distribution becomes close to normal. Empirical analysis of different transformations has shown that the most successful are the power transformations X → X h and their modifications. In this paper, we provide a symmetry-based explanation for this empirical success. 2017-09-28T04:27:12Z 2017-09-28T04:27:12Z 2016-01-01 Journal 16860209 2-s2.0-85008395342 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008395342&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42451
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
description	© 2016 by the Mathematical Association of Thailand. All rights reserved. A large number of efficient statistical methods have been designed for a frequent case when the distributions are normal (Gaussian). In practice, many probability distributions are not normal. In this case, Gaussian-based techniques cannot be directly applied. In many cases, however, we can apply these techniques indirectly – by first applying an appropriate transformation to the original variables, after which their distribution becomes close to normal. Empirical analysis of different transformations has shown that the most successful are the power transformations X → X h and their modifications. In this paper, we provide a symmetry-based explanation for this empirical success.
format	Journal
author	Dumrongpokaphan T. Barragan P. Kreinovich V.
spellingShingle	Dumrongpokaphan T. Barragan P. Kreinovich V. Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification
author_facet	Dumrongpokaphan T. Barragan P. Kreinovich V.
author_sort	Dumrongpokaphan T.
title	Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification
title_short	Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification
title_full	Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification
title_fullStr	Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification
title_full_unstemmed	Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification
title_sort	empirically successful transformations from non-gaussian to close-to-gaussian distributions: theoretical justification
publishDate	2017
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85008395342&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42451
_version_	1681422191752642560

Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification

Similar Items