This volume contains a selection of papers presented during the biennial meeting of the CLAssification and Data Analysis Group (CLADAG) of the Societa Italiana di Statistica which was orga­ nized by the Istituto di Statistica of the Universita degli Studi di Palermo and held in the Palazzo Steri in Palermo on July 5-6, 2001.

Classification Methods with Applications.- The STP Procedure as Overfitting Avoidance Tool in Classification Trees.- A Modal Symbolic Pattern Classifier.- Proximity Measures Between Classification Trees.- Ordinal Classification Trees Based on Impurity Measures.- Time Series Analysis and Related Methods.- Space Time Noisy Observation Smoothing.- Spectral Analysis in Frequency and Time Domain for Noisy Time Series.- A Resistant Measure of Heteroskedasticity in Explorative Time Series Analysis.- Computer Intensive Techniques and Algorithms.- Smoothing Score Algorithm for Generalized Additive Models.- Bootstrap Variables Selection in Neural Network Regression Models.- Robust Centre Location in Radial Basis Function Networks.- The Genetic Algorithm Estimates for the Parameters of Order p Normal Distributions.- Classification and Data Analysis in Economics.- Non-linear Dynamics in the Industrial Production Index.- Tensorial Co-Structure Analysis for the Full Multi Modules Customer Satisfaction Evaluation.- A Proposal of Classification of Wholesale Trade Enterprises on the Base of Structural and Performance Indicators.- The Analysis of Poverty in Italy: A Fuzzy Dynamic Approach.- Multivariate Analysis in Applied Sciences.- Combining Information from Several Experts: Selecting and Stopping Rules in Sequential Consulting.- A Spatial Clustering Hierarchical Model for Disease Mapping.- Second-order Interaction in a Trivariate Generalized Gamma Distribution.- Mortality and Air Pollution in Philadelphia: A Dynamic Generalized Linear Modelling Approach.- The Multivariate Adaptive Sampling for Estimating the Diversity in Biological Populations.- Adjusted Least Square Estimation for Noisy Images.- Flexible Dynamic Regression Models for Real-time Forecasting of Air Pollutant Concentration.

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