Titlebook: Classification and Multivariate Analysis for Complex Data Structures; Bernard Fichet,Domenico Piccolo,Maurizio Vichi Conference proceeding

Pillory

Efficient Incorporation of Additional Information to Classification RulesWe propose and discuss improved classification rules when a subset of the predictors is known to be ordered. We compare the performance of the new rules with other standard rules in a restricted normal setting using simulation experiments and real data exposing their good performance.

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Classification and Multivariate Analysis for Complex Data Structures978-3-642-13312-1Series ISSN 1431-8814 Series E-ISSN 2198-3321

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HATCH

https://doi.org/10.1007/978-3-031-09581-8exploratory tools of Multidimensional Data Analysis is the basis of different research strategies, proposed by the authors, combining the common estimation method with its geometrical representation. Here we present a systematic and unitary review of some of these methodologies by taking into accoun

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Engulf

Herco Fonteijn,Arie van der Lugtocess, and it plays a role in point process theory in most respects analogous to the normal distribution in the study of random variables. We first propose a statistical model for cluster analysis based on the homogeneous Poisson process. The clustering criterion is extracted from that model thanks

GOAT

Menno L. W. Knetsch,Thomas J. Cleijinterpretation issues. A TWO-CLASS tree methodoloy for non-parametric regression analysis is introduced. Data are as follows: a numerical response variable and a set of predictors (of categorical and/or numerical type) are measured on a sample of objects, with no probability assumption. Thus a non-p

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