On the interpretation of differences between groups for compositional data

Social polices are designed using information collected in surveys; such as the Catalan TimeUse survey. Accurate comparisons of time use data among population groups are commonlyanalysed using statistical methods. The total daily time expended on different activities by asingle person is equal to 24 hours. Because this type of data are compositional, its sample spacehas particular properties that statistical methods should respect. The critical points required tointerpret differences between groups are provided and described in terms of log-ratio methods.These techniques facilitate the interpretation of the relative differences detected in multivariateand univariate analysis

Compositional amalgamations and balances: a critical approach

The amalgamation operation is frequently used to reduce the number of parts of compositional data but it is a non-linear operation in the simplex with the usual geometry,the Aitchison geometry. The concept of balances between groups, a particular coordinate system designed over binary partitions of the parts, could be an alternative to theamalgamation in some cases. In this work we discuss the proper application of bothconcepts using a real data set corresponding to behavioral measures of pregnant sowsGeologische Vereinigung; Institut d’Estadística de Catalunya; International Association for Mathematical Geology; Càtedra Lluís Santaló d’Aplicacions de la Matemàtica; Generalitat de Catalunya, Departament d’Innovació, Universitats i Recerca; Ministerio de Educación y Ciencia; Ingenio 2010

Estadística en l'entorn informàtic: d'un ensenyament tradicional a l'ABP utilitzant la plataforma ACME

L’aprenentatge basat en problemes (ABP) es fonamenta en plantejar situacions que els alumnes probablement es trobaran en la vida real. Un ABP es caracteritza per tenir problemes acuradament seleccionats i dissenyats que requereixen l’adquisició de coneixement crític, habilitat en la resolució de problemes, estratègies d’aprenentatge autodirigides i capacitat de treball en grup. El projecte ACME (Avaluació Continuada i Millora de l’Ensenyament) té com a objectiu principal implementar un sistema eficient d’avaluació i treball continuat, mitjançant l’assignació de problemes de manera personalitzada per a cada alumne, oferint un sistema d’ajuda per a resoldre’ls, facilitant la comunicació professor-alumne i el seguiment i l’avaluació dels alumnes. En aquest treball es presenta una experiència d’adaptació a l’Espai Europeu d’Educació Superior (EEES) de les assignatures d’estadística de les enginyeries tècniques informàtiques de l’Escola Politècnica Superior de la UdG. Aquesta es basa en una concepció diferent de la manera d’ensenyar i aprendre l’estadística mitjançant la concreció de les competències i l’establiment de diverses activitats. Ens centrarem únicament en l’aplicació de la metodologia ABP i en la utilització de la plataforma e-learning ACM

The Dirichlet distribution with respect to the Aitchison measure on the simplex - a first approach

The algebraic-geometric structure of the simplex, known as Aitchison geometry, is usedto look at the Dirichlet family of distributions from a new perspective. A classicalDirichlet density function is expressed with respect to the Lebesgue measure on realspace. We propose here to change this measure by the Aitchison measure on thesimplex, and study some properties and characteristic measures of the resulting densityGeologische Vereinigung; Institut d’Estadística de Catalunya; International Association for Mathematical Geology; Patronat de l’Escola Politècnica Superior de la Universitat de Girona; Fundació privada: Girona, Universitat i Futur; Càtedra Lluís Santaló d’Aplicacions de la Matemàtica; Consell Social de la Universitat de Girona; Ministerio de Ciencia i Tecnología

A comparison of the alr and ilr transformations for kernel density estimation of compositional data

In a seminal paper, Aitchison and Lauder (1985) introduced classical kernel densityestimation techniques in the context of compositional data analysis. Indeed, they gavetwo options for the choice of the kernel to be used in the kernel estimator. One ofthese kernels is based on the use the alr transformation on the simplex SD jointly withthe normal distribution on RD-1. However, these authors themselves recognized thatthis method has some deficiencies. A method for overcoming these dificulties based onrecent developments for compositional data analysis and multivariate kernel estimationtheory, combining the ilr transformation with the use of the normal density with a fullbandwidth matrix, was recently proposed in Martín-Fernández, Chacón and Mateu-Figueras (2006). Here we present an extensive simulation study that compares bothmethods in practice, thus exploring the finite-sample behaviour of both estimatorsGeologische Vereinigung; Institut d’Estadística de Catalunya; International Association for Mathematical Geology; Càtedra Lluís Santaló d’Aplicacions de la Matemàtica; Generalitat de Catalunya, Departament d’Innovació, Universitats i Recerca; Ministerio de Educación y Ciencia; Ingenio 2010

Distributions on the simplex

The simplex, the sample space of compositional data, can be structured as a real Euclidean space. This fact allows to work with the coefficients with respect to an orthonormal basis. Over these coefficients we apply standard real analysis, inparticular, we define two different laws of probability trought the density function and we study their main propertiesGeologische Vereinigung; Universitat de Barcelona, Equip de Recerca Arqueomètrica; Institut d’Estadística de Catalunya; International Association for Mathematical Geology; Patronat de l’Escola Politècnica Superior de la Universitat de Girona; Fundació privada: Girona, Universitat i Futur

The normal distribution in some constrained sample spaces

Phenomena with a constrained sample space appear frequently in practice. This is the case e.g. with strictly positive data, or with compositional data, like percentages or proportions. If the natural measure of difference is not the absolute one, simple algebraic properties show that it is more convenient to work with a geometry different from the usual Euclidean geometry in real space, and with a measure different from the usual Lebesgue measure, leading to alternative models which better fit the phenomenon under study. The general approach is presented and illustrated using the normal distribution, both on the positive real line and on the D-part simplex. The original ideas of McAlister in his introduction to the lognormal distribution in 1879, are recovered and update

On the interpretation of differences between groups for compositional data

Social polices are designed using information collected in surveys; such as the Catalan TimeUse survey. Accurate comparisons of time use data among population groups are commonlyanalysed using statistical methods. The total daily time expended on different activities by asingle person is equal to 24 hours. Because this type of data are compositional, its sample spacehas particular properties that statistical methods should respect. The critical points required tointerpret differences between groups are provided and described in terms of log-ratio methods.These techniques facilitate the interpretation of the relative differences detected in multivariateand univariate analysisThis research was supported by the Ministerio de Econom´ıa y Competividad under the project “METRICS” Ref. MTM2012-33236; and by the Ag`encia de Gesti ´o d’Ajuts Universitaris i de Recerca of the Generalitat de Catalunya under the project Ref: 2014SGR55

On the interpretation of differences between groups for compositional data

Social polices are designed using information collected in surveys; such as the Catalan TimeUse survey. Accurate comparisons of time use data among population groups are commonlyanalysed using statistical methods. The total daily time expended on different activities by asingle person is equal to 24 hours. Because this type of data are compositional, its sample spacehas particular properties that statistical methods should respect. The critical points required tointerpret differences between groups are provided and described in terms of log-ratio methods.These techniques facilitate the interpretation of the relative differences detected in multivariateand univariate analysis

Log-ratio methods in mixture models for compositional data sets

When traditional methods are applied to compositional data misleading and incoherent results could be obtained. Finite mixtures of multivariate distributions are becoming increasingly important nowadays. In this paper, traditional strategies to fit a mixture model into compositional data sets are revisited and the major difficulties are detailed. A new proposal using a mixture of distributions defined on orthonormal log-ratio coordinates is introduced. A real data set analysis is presented to illustrate and compare the different methodologiesThis research was supported by the Ministerio de Economía y Competividad through the projects “METRICS” and “CoDa-RETOS” (MTM2012-33236; MTM2015-65016- C2-1-R: MINECO/FEDER,UE) and the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR: 2014SGR551