Compositional data analysis of vote shares in the 2001 Australian Federal election

First application of compositional data analysis techniques to Australian election data

Group preference structures in AHP–group decision making

This paper presents a procedure that allows us to determine the preference structures(PS) associated to each of the different groups of actors that can be identified in a groupdecision making problem with a large number of individuals. To that end, it makesuse of the Analytic Hierarchy Process (AHP) (Saaty, 1980) as the technique to solvediscrete multicriteria decision making problems. This technique permits the resolutionof multicriteria, multienvironment and multiactor problems in which subjective aspectsand uncertainty have been incorporated into the model, constructing ratio scales correspondingto the priorities relative to the elements being compared, normalised in adistributive manner (wi = 1). On the basis of the individuals’ priorities we identifydifferent clusters for the decision makers and, for each of these, the associated preferencestructure using, to that end, tools analogous to those of Multidimensional Scaling.The resulting PS will be employed to extract knowledge for the subsequent negotiationprocesses and, should it be necessary, to determine the relative importance of thealternatives being compared using anyone of the existing procedures

Regression analysis of compositional data when both the dependent variable and independent variable are components

It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features

The single principle of compositional data analysis, continuing fallacies, confusionsand misunderstandings and some suggested remedies

In any discipline, where uncertainty and variability are present, it is important to haveprinciples which are accepted as inviolate and which should therefore drive statisticalmodelling, statistical analysis of data and any inferences from such an analysis.Despite the fact that two such principles have existed over the last two decades andfrom these a sensible, meaningful methodology has been developed for the statisticalanalysis of compositional data, the application of inappropriate and/or meaninglessmethods persists in many areas of application. This paper identifies at least tencommon fallacies and confusions in compositional data analysis with illustrativeexamples and provides readers with necessary, and hopefully sufficient, arguments topersuade the culprits why and how they should amend their ways

Inserció i formació a la UdG: estudi sobre la 2a enquesta d'inserció laboral dels graduats universitaris (AQU Catalunya, 2005)

L'Agència per a la Qualitat del Sistema Universitari Català i les set universitats públiques catalanes analitzen regularment la inserció dels graduats universitaris en el món laboral. La darrera avaluació s'ha fet en forma d'enquesta, realitzada durant el 2005 als graduats el curs 2000-2001

Aula virtual de matemáticas.

Se aborda el uso de internet como herramienta en el proceso enseñanza aprendizaje en la asignatura de matemáticas en el centro de Enseñanza Secundaria Carlinda de Málaga. Los objetivos son: 1. Identificar las posibilidades reales de acceso de nuestros alumnos a las nuevas tecnologías incluyendo tanto la infraestructura de que disponen como su formación; 2. Estudio en profundidad de los recursos existentes actualmente en internet aplicables a la docencia en las asignaturas de matemáticas en niveles educativos no universitarios; 3. Crear un portal web que sirva de punto de encuentro de los alumnos en el aula virtual de matemáticas complemento de la tradicional clase presencial de matemáticas; 4. Iniciar al alumnado en el uso de las herramientas de comunicación vía internet como instrumento para el aprendizaje de las matemáticas de forma cooperativa; 5. Elaboración de materiales didácticos que permitan a los alumnos realizar prácticas con el ordenador. Uso de los programas de cálculo simbólico que permitan manipular los objetos matemáticos como medio para descubrir el conocimiento; 6. Utilización de los materiales didácticos para la integración de alumnos y alumnas con minusvalía; 7. Elaboración de tutoriales que incluyen test de autoevaluación para propiciar una ecuación autodidacta que permita llevar a cabo el proceso de enseñanza aprendizaje a distintos ritmos; 8. Propiciar la labor investigadora del alumnado, incluyendo en la web enlaces de interés que cautiven la curiosidad del alumno; 9. Propiciar un encuentro con la matemática aplicada, que destaque su carácter interdisciplinar. El proceso consta de tres fases: en la primera se realizó una encuesta al alumnado sobre las posibilidades reales de acceso a internet, su formación y su uso. Paralelamente se ha rastreado en internet para buscar otras experiencias parecidas que incluyesen la creación de páginas webs de contenidos matemáticos de nivel medio; en la segunda fase se han creado algunos materiales de trabajo para la inclusión en las webs. Básicamente hay dos tipos de actividades: guías para la utilización de programas de ordenador de cálculo simbólico y test de autoevaluación de modo de tutorial; la tercera fase ha consistido en el diseño y la creación de la web que integra las actividades realizadas en las fases anteriores con una serie de herramientas de comunicación. Los resultados concretos obtenidos han sido las actividades realizadas en el marco del proyecto: 1. Realización de una encuesta sobre las posibilidades reales de acceso a internet, su formación y su uso; 2. Relación de referencias de otras experiencias parecidas que incluyesen la creación de páginas webs de contenidos matemáticos de nivel medio; 3. Creación de materiales de trabajo para la inclusión en la webs: guías para la utilización de programas de ordenador de cálculo simbólico y test de autoevaluación a modo de tutorial; 4. Creación de la web que integra los materiales de trabajo junto a una serie de utilidades y herramientas de comunicación.

Guía del alumno : matemáticas I : Bachillerato de Ciencias de la Naturaleza y de la Salud, Educación a Distancia.

Material multicopiado con la colaboración del CPR Murcia II

Statistical modeling on (α, +∞)

Observations in daily practice are sometimes registered as positive values larger then a given threshold α. The sample space is in this case the interval (α,+∞), α > 0, which can be structured as a real Euclidean space in different ways. This fact opens the door to alternative statistical models depending not only on the assumed distribution function, but also on the metric which is considered as appropriate, i.e. the way differences are measured, and thus variability

Compositional data analysis: where we are and where should we be heading?

We take stock of the present position of compositional data analysis, of what has been achieved in the last 20 years, and then make suggestions as to what may be sensible avenues of future research. We take an uncompromisingly applied mathematical view, that the challenge of solving practical problems should motivate our theoretical research; and that any new theory should be thoroughly investigated to see if it may provide answers to previously abandoned practical considerations. Indeed a main theme of this lecture will be to demonstrate this applied mathematical approach by a number of challenging examples

Statistical modeling on coordinates

This paper is a first draft of the principle of statistical modelling on coordinates. Several causes —which would be long to detail—have led to this situation close to the deadline for submitting papers to CODAWORK’03. The main of them is the fast development of the approach along the last months, which let appear previous drafts as obsolete. The present paper contains the essential parts of the state of the art of this approach from my point of view. I would like to acknowledge many clarifying discussions with the group of people working in this field in Girona, Barcelona, Carrick Castle, Firenze, Berlin, G¨ottingen, and Freiberg. They have given a lot of suggestions and ideas. Nevertheless, there might be still errors or unclear aspects which are exclusively my fault. I hope this contribution serves as a basis for further discussions and new developments

Hilbert space on probability density functions with Aitchison geometry

Compositional data analysis motivated the introduction of a complete Euclidean structure in the simplex of D parts. This was based on the early work of J. Aitchison (1986) and completed recently when Aitchinson distance in the simplex was associated with an inner product and orthonormal bases were identified (Aitchison and others, 2002; Egozcue and others, 2003). A partition of the support of a random variable generates a composition by assigning the probability of each interval to a part of the composition. One can imagine that the partition can be refined and the probability density would represent a kind of continuous composition of probabilities in a simplex of infinitely many parts. This intuitive idea would lead to a Hilbert-space of probability densities by generalizing the Aitchison geometry for compositions in the simplex into the set probability densities

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 properties

When a data set can be considered compositional?

Traditionally, compositional data has been identified with closed data, and the simplex has been considered as the natural sample space of this kind of data. In our opinion, the emphasis on the constrained nature of compositional data has contributed to mask its real nature. More crucial than the constraining property of compositional data is the scale-invariant property of this kind of data. Indeed, when we are considering only few parts of a full composition we are not working with constrained data but our data are still compositional. We believe that it is necessary to give a more precise definition of composition. This is the aim of this oral contribution

Possible solution of some essential zero problems in compositional data analysis

One of the tantalising remaining problems in compositional data analysis lies in how to deal with data sets in which there are components which are essential zeros. By an essential zero we mean a component which is truly zero, not something recorded as zero simply because the experimental design or the measuring instrument has not been sufficiently sensitive to detect a trace of the part. Such essential zeros occur in many compositional situations, such as household budget patterns, time budgets, palaeontological zonation studies, ecological abundance studies. Devices such as nonzero replacement and amalgamation are almost invariably ad hoc and unsuccessful in such situations. From consideration of such examples it seems sensible to build up a model in two stages, the first determining where the zeros will occur and the second how the unit available is distributed among the non-zero parts. In this paper we suggest two such models, an independent binomial conditional logistic normal model and a hierarchical dependent binomial conditional logistic normal model. The compositional data in such modelling consist of an incidence matrix and a conditional compositional matrix. Interesting statistical problems arise, such as the question of estimability of parameters, the nature of the computational process for the estimation of both the incidence and compositional parameters caused by the complexity of the subcompositional structure, the formation of meaningful hypotheses, and the devising of suitable testing methodology within a lattice of such essential zero-compositional hypotheses. The methodology is illustrated by application to both simulated and real compositional data

Aitchison Geometry for Probability and Likelihood as a new approach to mathematical statistics

The Aitchison vector space structure for the simplex is generalized to a Hilbert space structure A2(P) for distributions and likelihoods on arbitrary spaces. Central notations of statistics, such as Information or Likelihood, can be identified in the algebraical structure of A2(P) and their corresponding notions in compositional data analysis, such as Aitchison distance or centered log ratio transform. In this way very elaborated aspects of mathematical statistics can be understood easily in the light of a simple vector space structure and of compositional data analysis. E.g. combination of statistical information such as Bayesian updating, combination of likelihood and robust M-estimation functions are simple additions/ perturbations in A2(Pprior). Weighting observations corresponds to a weighted addition of the corresponding evidence. Likelihood based statistics for general exponential families turns out to have a particularly easy interpretation in terms of A2(P). Regular exponential families form finite dimensional linear subspaces of A2(P) and they correspond to finite dimensional subspaces formed by their posterior in the dual information space A2(Pprior). The Aitchison norm can identified with mean Fisher information. The closing constant itself is identified with a generalization of the cummulant function and shown to be Kullback Leiblers directed information. Fisher information is the local geometry of the manifold induced by the A2(P) derivative of the Kullback Leibler information and the space A2(P) can therefore be seen as the tangential geometry of statistical inference at the distribution P. The discussion of A2(P) valued random variables, such as estimation functions or likelihoods, give a further interpretation of Fisher information as the expected squared norm of evidence and a scale free understanding of unbiased reasoning