A Comparison of Linear, Quadratic and Kernel Discriminant Rules for ECD Distributions Via ROC Analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F07%3A00032275" target="_blank" >RIV/00216224:14310/07:00032275 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
A Comparison of Linear, Quadratic and Kernel Discriminant Rules for ECD Distributions Via ROC Analysis
Original language description
The main approaches to discriminant analysis is considered with respect to the use of parametric models (based on the multivariate normal and elliptically contoured distributions), nonparametric models (based on the multivariate product Gaussian and polynomial kernels with various data-driven choices of the bandwidth) and also semiparametric models (based on the various kernel estimators for density functions of squared Mahalanobis distances). This paper demonstrates that linear and quadratic discriminant decision rules of classifying observation as coming from one of several multivariate normal distribution can be construct for much broader classes of distribution such as elliptical contoured distributions. While being much richer than the class of Gaussian distributions, the family of elliptically contoured distributions (ECD) inherits many of its attractive properties.
Czech name
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Czech description
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Publication year
2007
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
TIES 2007, 18th annual meeting of the International Environmetrics Society
ISBN
978-80-210-4333-6
ISSN
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e-ISSN
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Number of pages
1
Pages from-to
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Publisher name
Ivana Horová, Jiří Hřebíček
Place of publication
Brno
Event location
Mikulov
Event date
Aug 16, 2007
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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