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

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2007

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

1

Strana od-do

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Název nakladatele

Ivana Horová, Jiří Hřebíček

Místo vydání

Brno

Místo konání akce

Mikulov

Datum konání akce

16. 8. 2007

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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