[Statlist] Séminaires de Statistique - Institut de Statistique - Université de Neuchâtel

[KONDYLIS Atanassios]

> Séminaires de Statistique 
> Institut de Statistique, Université de Neuchâtel 
> Pierre à Mazel 7 (1er étage,salle 101), Neuchâtel,
> http://www2.unine.ch/statistics
> 
> Mardi 06 février 2007 à 11h00 
> Joe Whittaker, Department of Mathematics and Statistics, Lancaster University, England
> 
> Title : Weighted independence graphs for finite population surveys.
> 
> Abstract : The analysis of survey data, collected on a set of response variables defined over 
> a finite population, may benefit from a bird's eye view of their inter-relationships 
> and in particular, of their strengths. This overall analysis should highlight those variables that 
> strongly modify the conditional distribution of another variable, and by contrast, should indicate 
> those which have little affect. We introduce a weighted graph based on measures of independence 
> strength calculated from the population that fulfils this purpose. We show that the graph may be 
> properly defined in terms of population measures without any appeal to super populations, probability 
> modelling or to likelihood. A sample of young women and their smoking behaviours, taken from the 
> General Household Survey is used as an illustration.
> 
> --------------------------------------------------------------------------------------------------------------------------------
> 
> 
> Séminaires de Statistique 
> Institut de Statistique, Université de Neuchâtel 
> Pierre à Mazel 7 (1er étage,salle 101), Neuchâtel,
> http://www2.unine.ch/statistics
> 
> Mardi 13 février 2006 à 11h00 
> M. Kanevski, Institute of Geomatics and Analysis of Risk, University of Lausanne
> 
> Title : Machine learning algorithms for environmental and pollution data 
> 
> Abstract : The presentation deals with the description and application of machine learning algorithms - MLA for 
> environmental and pollution spatial (spatio-temporal) data.  The main approaches considered consist of traditional 
> artificial neural networks of different architectures (multilayer perceptron, general regression neural networks, 
> self-organising maps, etc.) and recent developments in statistical learning theory (Support Vector Machines, 
> Support Vector Regression) models. 
> 
> Variety of examples of MLA application both as an exploratory data analysis and modelling tools are given. 
> Current and future trends in MLA applications for environmental data are discussed. The results of MLA 
> are compared with geostatistical predictions/simulations. Case studies considered includes: soil and water 
> systems pollution, soil types and hydro-geological units classification, topo-climatic data modelling, optimisation 
> of monitoring networks and others. 
> 
>