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Universitat de Girona. Departament dâ€™InformÃ tica i MatemÃ tica Aplicada  
Aitchison, John  
Finite probability distributions and compositional data are mathematically similar, consisting of Ddimensional positive vectors with sum 1. Despite this similarity the meaningful forms of analysis in these different areas may require substantially different concepts and statistical modelling. This paper highlights these differences, but also poses the question of how such differences may contribute to understanding in the different areas. At CoDa workshops we have become so accustomed to, even obsessed with, modelling all compositional data problems within a simplex sample space together with its algebraicgeometric Hilbert space structure. The context of this Hilbert sample space is certainly often relevant to the formulation of a number of compositional data problems, but its mathematical elegance should not override appropriate meaningful statistical modelling to resolve the real compositional problem. In this paper I illustrate some relevant modelling by consideration of how a variety of persons differ in their ability to perform inferential tasks particularly in the process of differential diagnosis  
http://hdl.handle.net/2072/299049  
eng  
Universitat de Girona. Departament dâ€™InformÃ tica i MatemÃ tica Aplicada  
Tots els drets reservats  
EstadÃstica matemÃ tica  Congressos
Mathematical statistics  Congresses AnÃ lisi multivariable  Congressos Multivariate analysis  Congresses DistribuciÃ³ (Teoria de la probabilitat)  Congressos Distribution (Probability theory)  Congresses 

Statistical Modelling of Compositional Problems Involving Finite Probability Distributions  
info:eurepo/semantics/conferenceObject  
Recercat 