1982/01/01 by J. Aitchison · 4 citations
Computer Science · #Geochemistry and Geologic Mapping #Compositional data #Computer science #Machine learning
paper · doi:10.1111/j.2517-6161.1982.tb01195.x
openalex publication_date 1982/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Summary The simplex plays an important role as sample space in many practical situations where compositional data, in the form of proportions of some whole, require interpretation. It is argued that the statistical analysis of such data has proved difficult because of a lack both of concepts of independence and of rich enough parametric classes of distributions in the simplex. A variety of independence hypotheses are introduced and interrelated, and new classes of transformed-normal distributions in the simplex are provided as models within which the independence hypotheses can be tested through standard theory of parametric hypothesis testing. The new concepts and statistical methodology are illustrated by a number of applications.