Analysis

New PDF release: Advances in Multivariate Data Analysis: Proceedings of the

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

This quantity features a number of papers offered throughout the biennial assembly of the type and knowledge research staff (CLADAG) of the Societa Italiana di Statistica which was once 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 range of issues from multivariate info research, with particular emphasis on class and clustering, computa­ tional information, time sequence research, and purposes in quite a few classical or fresh domain names. A two-fold cautious reviewing technique ended in the choice of twenty-two papers that are offered during this vol­ ume. they communicate both a brand new concept or technique, current a brand new set of rules, or main issue an enticing program. we've got clustered those papers into 5 teams as follows: 1. type equipment with purposes 2. Time sequence research and similar equipment three. desktop in depth recommendations and Algorithms four. class 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 show their gratitude to the authors whose enthusiastic participation made the assembly attainable and intensely 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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Space Time Noisy Observ ation Smoothing 3 59 Maximum Likelihood Estimation The Kalman filter can be performed only when all the parameters of the state sp ace model ar e known . To this purpose, we pr esent here a maximum likelih ood est imat or of such paramet ers. If t he initial values of the process ar e equal to t heir un conditional mean , t he STARG model can also be writt en as follows (6) II(Y - jj) = ii with II = (IT ® A o) - p L: (Bh h=l ® A h ) (where the T xT matrix Bh has all zero ent ries except for t he h - th lower diagon al , with element s equal t o one) , Y= vec(Y) , Y = {Y(- , t) , t = 1, '" ,T }, u=vec(u) , u = {u(· , t) , t = 1, ' " , T }.

ApY(· , t - p) = uf -, t) . , 1998). , Romagnoli L. , t) (5) where H = [A 01 G- .. 0] is the measurement matrix. Given the state space formulation, it is straightforward to apply the Kalman filter which, computing the optimal recursive estimation of the state vector at time t , leads to the implementation of algorithms for filtering and smoothing. Space Time Noisy Observ ation Smoothing 3 59 Maximum Likelihood Estimation The Kalman filter can be performed only when all the parameters of the state sp ace model ar e known .

Ordinal Classification Tr ees Bas ed on Impurity Measures 47 Table 1. Joint distribution of (Y, Y(LlHo » Y\Y(L1Ho ) 1 1 26 2 1 2 3 4 0 5 o o 6 29 2 4 3 0 1 0 (60) 13 (34) 4 0 11 (5) 61 (53) 4 1 10 (5) 13 (12) 59 (61) 2 0 2 (0) 0 28 1 (0) 13 (9) 0 (6) 1 104 102 32 68 0 82 5 6 0 2 (6) 2 (4) 3 (18) o (0) 53 60 (3) (11) (5) (12) (53) 27 102 81 87 30 68 395 Table 2. J oint distribution of (Y, Y(LlSo» Y\Y(L1So ) 1 2 3 4 5 6 1 21 6 7 3 0 0 37 2 3 87 36 12 0 4 142 3 0 0 17 0 0 1 18 4 0 4 7 62 0 7 80 5 2 2 8 4 18 0 34 6 1 3 6 6 12 56 84 27 102 81 87 30 68 395 In Tabl e 1 the joint distribution of the response vari abl e, Y , and th e predictor Y (L1H0) is represent ed.

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