Overview
- Presents mathematical and statistical theory and techniques in compositional data analysis
- Explores the wealth of applications of CoDa in geochemistry, the life sciences and other disciplines
- Features cutting-edge, authoritative contributions on CoDa
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 187)
Included in the following conference series:
Conference proceedings info: CoDaWork 2015.
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Table of contents (12 papers)
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Front Matter
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Compositional Data Analysis
Keywords
About this book
The authoritative contributions gathered in this volume reflect the state of the art in compositional data analysis (CoDa). The respective chapters cover all aspects of CoDa, ranging from mathematical theory, statistical methods and techniques to its broad range of applications in geochemistry, the life sciences and other disciplines. The selected and peer-reviewed papers were originally presented at the 6th International Workshop on Compositional Data Analysis, CoDaWork 2015, held in L’Escala (Girona), Spain.
Compositional data is defined as vectors of positive components and constant sum, and, more generally, all those vectors representing parts of a whole which only carry relative information. Examples of compositional data can be found in many different fields such as geology, chemistry, economics, medicine, ecology and sociology. As most of the classical statistical techniques are incoherent on compositions, in the 1980s John Aitchison proposed the log-ratio approachto CoDa. This became the foundation of modern CoDa, which is now based on a specific geometric structure for the simplex, an appropriate representation of the sample space of compositional data.
The International Workshops on Compositional Data Analysis offer a vital discussion forum for researchers and practitioners concerned with the statistical treatment and modelling of compositional data or other constrained data sets and the interpretation of models and their applications. The goal of the workshops is to summarize and share recent developments, and to identify important lines of future research.
Editors and Affiliations
About the editors
Josep Antoni Martín-Fernández holds a degree in Mathematics. He received his PhD from the Polytechnic University of Catalonia, where he worked on measurements of difference and the non-parametric classification of compositional data. He is currently a Professor at the Department of Computer Science, Applied Mathematics and Statistics of the University of Girona, Spain. His interests primarily lie in the statistical analysis of compositional data, where he focuses on cluster analysis, rounded zeros and missing data.
Santiago Thió-Henestrosa holds a PhD in Computer Science from the Polytechnic University of Catalonia. He is a Professor at the Department of Computer Science, Applied Mathematics and Statistics of the University of Girona, Spain. He organized the first Compositional Data Analysis Workshop in 2003 and is the author of CoDaPack, currently the only user-friendly software available for Compositional Data Analysis.
Bibliographic Information
Book Title: Compositional Data Analysis
Book Subtitle: CoDaWork, L’Escala, Spain, June 2015
Editors: Josep Antoni Martín-Fernández, Santiago Thió-Henestrosa
Series Title: Springer Proceedings in Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-319-44811-4
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-44810-7Published: 20 November 2016
Softcover ISBN: 978-3-319-83141-1Published: 28 June 2018
eBook ISBN: 978-3-319-44811-4Published: 19 November 2016
Series ISSN: 2194-1009
Series E-ISSN: 2194-1017
Edition Number: 1
Number of Pages: X, 209
Number of Illustrations: 13 b/w illustrations, 58 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Geochemistry, Statistics for Life Sciences, Medicine, Health Sciences