Conference Guide -> download pdf (includes scientific and social program)
Conference Proceedings -> download pdf (ISBN: 978-3-200-03103-6)
Oral presentations -> download zip (82 MB)
CoDaWork 2013: Musical Moments -> youtube (thanks to Michael Greenacre!); text of last song: Correlation
CoDaWork, June 3-7, 2013, Vorau, Austria
CoDaWork 2013, the fifth international Workshop on Compositional Data analysis, offers a forum of discussion for people concerned with the statistical treatment and modelling of compositional data or other constrained data sets, and the interpretation of models or applications involving them. The primary goal of the workshop is to identify important potential lines of future research and gain insight as to how they might be tackled.
CoDaWork 2013 intends to bring together specialist researchers, data analysts, postgraduate students, as well as those with a general interest in the field, to summarize and share their contributions and recent developments.
Statisticians, researchers and practitioners from all areas are invited to contribute to the meeting with a talk and/or poster. All submitted abstracts will undergo a reviewing process. The conference language is English.
Call for Papers: pdf
Abstract submission: until January 25, 2013.
Notification of abstract acceptance: until February 11, 2013.
If you need any help, please contact: email@example.com
We have reserved space for exhibitions of companies and institutions. If you are interested, please contact us via the above email address.
On compositional data:
Compositional Data (CoDa) are typically defined as vectors of positive components and constant sum, usually 100% or 1. These conditions render most classical statistical techniques useless on compositions, as they were devised for unbounded real vectors. However, there are many more types of data having the same limitations: as soon as the variables of a data set show the relative importance of some parts of a whole, data must be considered compositional. Examples of disguised compositions are data presented in ppm, ppb, molarities, or any other concentration units. John Aitchison introduced the log-ratio approach to analyse CoDa back in the eighties. His solution was based on transforming the data vector with some log-ratio transformations, and applying classical techniques to the scores so obtained. This became the foundation of modern CoDa analysis, nowadays based on an own geometric structure for the simplex, the sample space of CoDa. The validity of these considerations is not restricted to CoDa: there are many more data sets for which the sample space has some constraint, suggesting that they need an alternative, more meaningful geometric structure. Examples abound in the natural and social sciences: vectors of positive amounts, functional data, spherical data, ordered variables, etc. CoDa analysis insights may be of good use to scientists working with these data sets, and vice versa.