Antropologia filosòfica i pedagogia en M. Foucault

Anàlisi del pensament de M. Focault

[Ressenya del llibre Què farem quan siguem petits?, de Penny Ritscher]

Des de la ciutat italiana de Reggio Emilia, narrat de forma planera, propera i reflexiva, ens arriba aquest llibre de la boca d'una mestra amb experiència, amb l'objectiu d'apropar-nos a una versió actual del pensament pedagògic

Història urbana de Girona: una història dibuixada

El Dr. Josep Maria Nolla ha contribuït de manera decisiva al coneixement dels orígens de la ciutat de Girona. El seu treball i el dels seus companys en aquesta aventura de recerca és a l'abast de tothom en la col·lecció "Història urbana de Girona", que ha tingut, la tardor del 2007, una traducció brillant en l'exposició "De Kerunta a Gerunda. Els orígens de la ciutat"

Biotech Girona: l'espai de la biotecnologia

Espais a mida, tota mena de facilitats i bona companyia. Sembla l'anunci d'una promoció immobiliària, però això és el que ofereix el Biogech Girona, un lloc dins el Parc Científic i Tecnològic que proposa espais a mida per establir-se en un entorn d'excel·lència en la recerca. El Parc acaba d'obrir la convocatòria per seleccionar les empreses interessades

Batxillerat: camins cap a la Universitat

El batxillerat és un intens camí que recorre l'estudiant i que en bona part dels casos té com a meta la universitat. En el trajecte hi ha uns quants esculls, com el treball de recerca i la selectivitat. Els joves han de prémer fort l'accelerador si volen assolir la fita consignada. El suport de la família i els professors els pot fer el viatge una mica més fàcil

Medicina a Girona

La Universitat de Girona impartirà la titulació de Medicina en una facultat pròpia a partir del curs 2008-2009. Es proposa un pla d'estudis innovador que la convertirà en un centre pioner a Catalunya, elaborat en complicitat amb els diversos agents clínics i assistencials de la demarcació, per garantir la màxima qualitat de la docència i la recerca

Física bàsica per a la universitat [versió html]

Llibre preparat per ser utilitzat com a material de text de Física bàsica, assignatura de lliure elecció de 6 crèdits, del segon quadrimestre dels nous plans d'estudis d' Informàtica Tècnica (especialitats de Sistemes i de Gestió) i d' Enginyeria Tècnica Agrícola (especialitats d' Explotacions Agropecuàries i d' Indústries Agràries i Agroalimentàries) de la Universitat de Girona. L’objectiu estratègic és donar als alumnes, en un sol quadrimestre, la formació necessària per a seguir un curs de primer de física, així com que l’alumne tingui agilitat en la transformació d’unitats, s’habituï a comprovar la raonabilitat dels resultats i sigui capaç de superar la noció que la física és un conjunt de formules inconnexes

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

Some last thoughts on compositional data analysis

One of the disadvantages of old age is that there is more past than future: this, however, may be turned into an advantage if the wealth of experience and, hopefully, wisdom gained in the past can be reflected upon and throw some light on possible future trends. To an extent, then, this talk is necessarily personal, certainly nostalgic, but also self critical and inquisitive about our understanding of the discipline of statistics. A number of almost philosophical themes will run through the talk: search for appropriate modelling in relation to the real problem envisaged, emphasis on sensible balances between simplicity and complexity, the relative roles of theory and practice, the nature of communication of inferential ideas to the statistical layman, the inter-related roles of teaching, consultation and research. A list of keywords might be: identification of sample space and its mathematical structure, choices between transform and stay, the role of parametric modelling, the role of a sample space metric, the underused hypothesis lattice, the nature of compositional change, particularly in relation to the modelling of processes. While the main theme will be relevance to compositional data analysis we shall point to substantial implications for general multivariate analysis arising from experience of the development of compositional data analysis…

Compositional Data in Biomedical Research

Modern methods of compositional data analysis are not well known in biomedical research. Moreover, there appear to be few mathematical and statistical researchers working on compositional biomedical problems. Like the earth and environmental sciences, biomedicine has many problems in which the relevant scienti c information is encoded in the relative abundance of key species or categories. I introduce three problems in cancer research in which analysis of compositions plays an important role. The problems involve 1) the classi cation of serum proteomic pro les for early detection of lung cancer, 2) inference of the relative amounts of di erent tissue types in a diagnostic tumor biopsy, and 3) the subcellular localization of the BRCA1 protein, and it's role in breast cancer patient prognosis. For each of these problems I outline a partial solution. However, none of these problems is \solved". I attempt to identify areas in which additional statistical development is needed with the hope of encouraging more compositional data analysts to become involved in biomedical research

Spatial Analysis of Cell Composition Data

A version of Matheron’s discrete Gaussian model is applied to cell composition data. The examples are for map patterns of felsic metavolcanics in two different areas. Q-Q plots of the model for cell values representing proportion of 10 km x 10 km cell area underlain by this rock type are approximately linear, and the line of best fit can be used to estimate the parameters of the model. It is also shown that felsic metavolcanics in the Abitibi area of the Canadian Shield can be modeled as a fractal

Conditional compositional biplots: theory and application

The biplot has proved to be a powerful descriptive and analytical tool in many areas of applications of statistics. For compositional data the necessary theoretical adaptation has been provided, with illustrative applications, by Aitchison (1990) and Aitchison and Greenacre (2002). These papers were restricted to the interpretation of simple compositional data sets. In many situations the problem has to be described in some form of conditional modelling. For example, in a clinical trial where interest is in how patients’ steroid metabolite compositions may change as a result of different treatment regimes, interest is in relating the compositions after treatment to the compositions before treatment and the nature of the treatments applied. To study this through a biplot technique requires the development of some form of conditional compositional biplot. This is the purpose of this paper. We choose as a motivating application an analysis of the 1992 US President ial Election, where interest may be in how the three-part composition, the percentage division among the three candidates - Bush, Clinton and Perot - of the presidential vote in each state, depends on the ethnic composition and on the urban-rural composition of the state. The methodology of conditional compositional biplots is first developed and a detailed interpretation of the 1992 US Presidential Election provided. We use a second application involving the conditional variability of tektite mineral compositions with respect to major oxide compositions to demonstrate some hazards of simplistic interpretation of biplots. Finally we conjecture on further possible applications of conditional compositional biplots

Analyzing shapes as compositions of distances

We propose to analyze shapes as “compositions” of distances in Aitchison geometry as an alternate and complementary tool to classical shape analysis, especially when size is non-informative. Shapes are typically described by the location of user-chosen landmarks. However the shape – considered as invariant under scaling, translation, mirroring and rotation – does not uniquely define the location of landmarks. A simple approach is to use distances of landmarks instead of the locations of landmarks them self. Distances are positive numbers defined up to joint scaling, a mathematical structure quite similar to compositions. The shape fixes only ratios of distances. Perturbations correspond to relative changes of the size of subshapes and of aspect ratios. The power transform increases the expression of the shape by increasing distance ratios. In analogy to the subcompositional consistency, results should not depend too much on the choice of distances, because different subsets of the pairwise distances of landmarks uniquely define the shape. Various compositional analysis tools can be applied to sets of distances directly or after minor modifications concerning the singularity of the covariance matrix and yield results with direct interpretations in terms of shape changes. The remaining problem is that not all sets of distances correspond to a valid shape. Nevertheless interpolated or predicted shapes can be backtransformated by multidimensional scaling (when all pairwise distances are used) or free geodetic adjustment (when sufficiently many distances are used)

Comparing correspondence analysis and the log-ratio alternative for representing categorical data

We compare correspondance análisis to the logratio approach based on compositional data. We also compare correspondance análisis and an alternative approach using Hellinger distance, for representing categorical data in a contingency table. We propose a coefficient which globally measures the similarity between these approaches. This coefficient can be decomposed into several components, one component for each principal dimension, indicating the contribution of the dimensions to the difference between the two representations. These three methods of representation can produce quite similar results. One illustrative example is given

CoDa-dendrogram: A new exploratory too

The use of orthonormal coordinates in the simplex and, particularly, balance coordinates, has suggested the use of a dendrogram for the exploratory analysis of compositional data. The dendrogram is based on a sequential binary partition of a compositional vector into groups of parts. At each step of a partition, one group of parts is divided into two new groups, and a balancing axis in the simplex between both groups is defined. The set of balancing axes constitutes an orthonormal basis, and the projections of the sample on them are orthogonal coordinates. They can be represented in a dendrogram-like graph showing: (a) the way of grouping parts of the compositional vector; (b) the explanatory role of each subcomposition generated in the partition process; (c) the decomposition of the total variance into balance components associated with each binary partition; (d) a box-plot of each balance. This representation is useful to help the interpretation of balance coordinates; to identify which are the most explanatory coordinates; and to describe the whole sample in a single diagram independently of the number of parts of the sample