Advances in Multivariate Data Analysis: Proceedings of the by Carmela Cappelli, Francesco Mola (auth.), Prof. Dr.

By Carmela Cappelli, Francesco Mola (auth.), Prof. Dr. Hans-Hermann Bock, Prof. Marcello Chiodi, Prof. Antonino Mineo (eds.)

This quantity features a choice of papers offered throughout the biennial assembly of the category and information research team (CLADAG) of the Societa Italiana di Statistica which used to be orga­ nized by way of the Istituto di Statistica of the Universita degli Studi di Palermo and held within the Palazzo Steri in Palermo on July 5-6, 2001. For this convention, and after checking the submitted four­ web page abstracts, fifty four papers have been admitted for presentation. They coated a wide variety of issues from multivariate info research, with precise emphasis on type and clustering, computa­ tional facts, time sequence research, and functions in a variety of classical or fresh domain names. A two-fold cautious reviewing strategy resulted in the choice of twenty-two papers that are awarded during this vol­ ume. they impart both a brand new notion or method, current a brand new set of rules, or drawback an engaging program. we've clustered those papers into 5 teams as follows: 1. category equipment with functions 2. Time sequence research and comparable equipment three. laptop extensive thoughts and Algorithms four. type and information research in Economics five. Multivariate research in technologies. In each one part the papers are prepared in alphabetical order. The editors - of them the organizers of the CLADAG confer­ ence - wish to convey their gratitude to the authors whose enthusiastic participation made the assembly attainable and extremely successful.

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Extra resources for Advances in Multivariate Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Palermo, July 5–6, 2001

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Wadsworth, Belmont, California. CAPPELLI , C. D. (2000) : An MLE Strategy for Combining Optimally Pruned Decision Trees . COMPSTAT 2000, 235 - 246, Phisica Verlag. , MOLA , F. and SICILIANO R . (2002) : A Statistical Approach to Growing a Reli able Hone st Tree . Computational Statistics and Data Analysis, 38, 285-299 . CIAMPI, A. and THIFFAULT , J . (1988) : Recursive Partition in Biostatistics: Stability of Trees and Choice of the Most Stable Classification, COMPSTAT 1988, Physica Verlag. B. and MALLOWS , C.

Ordinal Classification Trees Based on Impurity Measures 51 GINI , C. (1954) : Variabilita e concentrazione . Veschi, Roma. MOLA , F . and SIC ILIANO , R . (1997) : A Fast Splitting Procedure for Classification Trees , Sta tistics and Computing, 7, 209-216. MOLA , F . and SICILIANO, R . (1998) : A Gen eral Splitting Criterion for Classification Tree s, M etron , 56, 156-171. P ICCARRETA , R. (2001) : A New Measure of Nominal-Ordinal Associ at ion, Jour nal of Applied Statistics, 28, 107-120. -S .

In this case we have selected some example situations to give a flavour of the types of behaviour that our estimator exhibit under particular conditions. Section 4 shows an application to image sequence analysis. The data set is provided by the Goddard Distributed Archiv e Center (GDAC) , and consists of Earth images derived through Normalized Difference Vegetation Index (NDVI) to study the vegetation dynamics around the globe. At last, Section 5 closes the paper with som e final considerations on AMLE-ST.

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