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General Blending Models for Data From Mixture Experiments
We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4673519/ https://www.ncbi.nlm.nih.gov/pubmed/26681812 http://dx.doi.org/10.1080/00401706.2014.947003 |
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author | Brown, L. Donev, A. N. Bissett, A. C. |
author_facet | Brown, L. Donev, A. N. Bissett, A. C. |
author_sort | Brown, L. |
collection | PubMed |
description | We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online. |
format | Online Article Text |
id | pubmed-4673519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-46735192015-12-15 General Blending Models for Data From Mixture Experiments Brown, L. Donev, A. N. Bissett, A. C. Technometrics Original Articles We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online. Taylor & Francis 2015-10-02 2015-11-16 /pmc/articles/PMC4673519/ /pubmed/26681812 http://dx.doi.org/10.1080/00401706.2014.947003 Text en © 2015 The Author(s). Published with license by Taylor & Francis http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The moral rights of the named author(s) have been asserted. |
spellingShingle | Original Articles Brown, L. Donev, A. N. Bissett, A. C. General Blending Models for Data From Mixture Experiments |
title | General Blending Models for Data From Mixture Experiments |
title_full | General Blending Models for Data From Mixture Experiments |
title_fullStr | General Blending Models for Data From Mixture Experiments |
title_full_unstemmed | General Blending Models for Data From Mixture Experiments |
title_short | General Blending Models for Data From Mixture Experiments |
title_sort | general blending models for data from mixture experiments |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4673519/ https://www.ncbi.nlm.nih.gov/pubmed/26681812 http://dx.doi.org/10.1080/00401706.2014.947003 |
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