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Editorial, special issue on “Advances in Robust Statistics”

Starting with 2020 volume, the journal Metron has decided to celebrate the centenary since its foundation with three special issues. This volume is dedicated to robust statistics. A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to out...

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Detalles Bibliográficos
Autores principales: Riani, Marco, Hubert, Mia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Milan 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236743/
https://www.ncbi.nlm.nih.gov/pubmed/34219810
http://dx.doi.org/10.1007/s40300-021-00213-w
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author Riani, Marco
Hubert, Mia
author_facet Riani, Marco
Hubert, Mia
author_sort Riani, Marco
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description Starting with 2020 volume, the journal Metron has decided to celebrate the centenary since its foundation with three special issues. This volume is dedicated to robust statistics. A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to outliers or to other departures from the postulated model. Robust statistical methods provide useful tools for reducing this sensitivity, through the detection of the outliers by first fitting the majority of the data and then by flagging deviant data points. The six papers in this issue cover a wide orientation in all fields of robustness. This editorial first provides some facts about the history and current state of robust statistics and then summarizes the contents of each paper.
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spelling pubmed-82367432021-06-28 Editorial, special issue on “Advances in Robust Statistics” Riani, Marco Hubert, Mia Metron Article Starting with 2020 volume, the journal Metron has decided to celebrate the centenary since its foundation with three special issues. This volume is dedicated to robust statistics. A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to outliers or to other departures from the postulated model. Robust statistical methods provide useful tools for reducing this sensitivity, through the detection of the outliers by first fitting the majority of the data and then by flagging deviant data points. The six papers in this issue cover a wide orientation in all fields of robustness. This editorial first provides some facts about the history and current state of robust statistics and then summarizes the contents of each paper. Springer Milan 2021-06-28 2021 /pmc/articles/PMC8236743/ /pubmed/34219810 http://dx.doi.org/10.1007/s40300-021-00213-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Riani, Marco
Hubert, Mia
Editorial, special issue on “Advances in Robust Statistics”
title Editorial, special issue on “Advances in Robust Statistics”
title_full Editorial, special issue on “Advances in Robust Statistics”
title_fullStr Editorial, special issue on “Advances in Robust Statistics”
title_full_unstemmed Editorial, special issue on “Advances in Robust Statistics”
title_short Editorial, special issue on “Advances in Robust Statistics”
title_sort editorial, special issue on “advances in robust statistics”
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236743/
https://www.ncbi.nlm.nih.gov/pubmed/34219810
http://dx.doi.org/10.1007/s40300-021-00213-w
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