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Semiautomatic robust regression clustering of international trade data
The purpose of this paper is to show in regression clustering how to choose the most relevant solutions, analyze their stability, and provide information about best combinations of optimal number of groups, restriction factor among the error variance across groups and level of trimming. The procedur...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193608/ https://www.ncbi.nlm.nih.gov/pubmed/34131421 http://dx.doi.org/10.1007/s10260-021-00569-3 |
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author | Torti, Francesca Riani, Marco Morelli, Gianluca |
author_facet | Torti, Francesca Riani, Marco Morelli, Gianluca |
author_sort | Torti, Francesca |
collection | PubMed |
description | The purpose of this paper is to show in regression clustering how to choose the most relevant solutions, analyze their stability, and provide information about best combinations of optimal number of groups, restriction factor among the error variance across groups and level of trimming. The procedure is based on two steps. First we generalize the information criteria of constrained robust multivariate clustering to the case of clustering weighted models. Differently from the traditional approaches which are based on the choice of the best solution found minimizing an information criterion (i.e. BIC), we concentrate our attention on the so called optimal stable solutions. In the second step, using the monitoring approach, we select the best value of the trimming factor. Finally, we validate the solution using a confirmatory forward search approach. A motivating example based on a novel dataset concerning the European Union trade of face masks shows the limitations of the current existing procedures. The suggested approach is initially applied to a set of well known datasets in the literature of robust regression clustering. Then, we focus our attention on a set of international trade datasets and we provide a novel informative way of updating the subset in the random start approach. The Supplementary material, in the spirit of the Special Issue, deepens the analysis of trade data and compares the suggested approach with the existing ones available in the literature. |
format | Online Article Text |
id | pubmed-8193608 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-81936082021-06-11 Semiautomatic robust regression clustering of international trade data Torti, Francesca Riani, Marco Morelli, Gianluca Stat Methods Appt Original Article The purpose of this paper is to show in regression clustering how to choose the most relevant solutions, analyze their stability, and provide information about best combinations of optimal number of groups, restriction factor among the error variance across groups and level of trimming. The procedure is based on two steps. First we generalize the information criteria of constrained robust multivariate clustering to the case of clustering weighted models. Differently from the traditional approaches which are based on the choice of the best solution found minimizing an information criterion (i.e. BIC), we concentrate our attention on the so called optimal stable solutions. In the second step, using the monitoring approach, we select the best value of the trimming factor. Finally, we validate the solution using a confirmatory forward search approach. A motivating example based on a novel dataset concerning the European Union trade of face masks shows the limitations of the current existing procedures. The suggested approach is initially applied to a set of well known datasets in the literature of robust regression clustering. Then, we focus our attention on a set of international trade datasets and we provide a novel informative way of updating the subset in the random start approach. The Supplementary material, in the spirit of the Special Issue, deepens the analysis of trade data and compares the suggested approach with the existing ones available in the literature. Springer Berlin Heidelberg 2021-06-11 2021 /pmc/articles/PMC8193608/ /pubmed/34131421 http://dx.doi.org/10.1007/s10260-021-00569-3 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 | Original Article Torti, Francesca Riani, Marco Morelli, Gianluca Semiautomatic robust regression clustering of international trade data |
title | Semiautomatic robust regression clustering of international trade data |
title_full | Semiautomatic robust regression clustering of international trade data |
title_fullStr | Semiautomatic robust regression clustering of international trade data |
title_full_unstemmed | Semiautomatic robust regression clustering of international trade data |
title_short | Semiautomatic robust regression clustering of international trade data |
title_sort | semiautomatic robust regression clustering of international trade data |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193608/ https://www.ncbi.nlm.nih.gov/pubmed/34131421 http://dx.doi.org/10.1007/s10260-021-00569-3 |
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