Applied Statistics 2009
International Conference

September 20 - 23, 2009
Ribno (Bled), Slovenia

Next conference AS2010
September 19 - 22, 2010

Past conferences:
Applied Statistics 2008
Applied Statistics 2007

Applied Statistics 2006
Applied Statistics 2005
Applied Statistics 2004
Conferences before 2004



  Important dates

  Acceptance note
     Sent to authors
  Registration and
  hotel reservation
      July 1
  Reduced early
  payment fee

      July 15


Slovenian Research Agency (ARRS)



Statistics of compositional data

 Gerald van den Boogaart

 Technical University Freiberg, Germany

The talk gives a short introduction into the recent approach to the statistical analysis of compositional data.  Data providing the amounts of different components forming a total is call compositional if the total amount is irrelevant for statistical question under consideration. This might be amounts of different elements in minerals, the amounts of different cell types in a blood samples, the relative amounts of different beetle species in ecological systems, or the money spend on different types of expenses (workforce, tax, operation costs, raw products) in companies. Almost never all relevant components are reported. 

Seen from a classical view of multivariate statistics this type of data has a lot of strange properties: it can't be normally distributed because the domain is bounded to a triangle like region, variances matrices are always singular, scaled vectors correspond to the same composition, a single measurement error or missing value changes all other values, relative errors are more relevant than absolute differences, different units of measure (e.g. mass %, vol %) or different unobserved components can lead to different order relations and directions of dependence among the unaffected components, data is practically always heavily skewed. 

The talk will introduce you to a solution to that problem: The principle of working in coordinates. This principle allows it to translate compositional problems into classical multivariate statistical tasks, to do a consistent analysis with well known methods and to translate the results back into a compositional context. It will show this principle at work for some classical methods like distributions, linear models, tests, principle component analysis and outlier detection. And it we show how new a specialized methodology can be built on that. The aim is to show how simple it can be to analyze compositional data in a consistent way avoiding all the paradoxes and artefacts mentioned above, when we just follow some basic rules.

 Applied Statistics 2009                                e-mail: