The minimum weighted covariance determinant estimator for high-dimensional data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00546694" target="_blank" >RIV/67985556:_____/22:00546694 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/22:00546601

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11634-021-00471-6" target="_blank" >https://link.springer.com/article/10.1007/s11634-021-00471-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11634-021-00471-6" target="_blank" >10.1007/s11634-021-00471-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The minimum weighted covariance determinant estimator for high-dimensional data

Popis výsledku v původním jazyce

In a variety of diverse applications, it is very desirable to perform a robust analysis of high-dimensional measurements without being harmed by the presence of a possibly larger percentage of outlying measurements. The minimum weighted covariance determinant (MWCD) estimator, based on implicit weights assigned to individual observations, represents a promising and flexible extension of the popular minimum covariance determinant (MCD) estimator of the expectation and scatter matrix of mlutivariate data. In this work, a regularized version of the MWCD denoted as the minimum regularized weighted covariance determinant (MRWCD) estimator is proposed. At the same time, it is accompanied by an outlier detection procedure. The novel MRWCD estimator is able to outperform other available robust estimators in several simulation scenarios, especially in estimating the scatter matrix of contaminated high-dimensional data.

Název v anglickém jazyce

The minimum weighted covariance determinant estimator for high-dimensional data

Popis výsledku anglicky

In a variety of diverse applications, it is very desirable to perform a robust analysis of high-dimensional measurements without being harmed by the presence of a possibly larger percentage of outlying measurements. The minimum weighted covariance determinant (MWCD) estimator, based on implicit weights assigned to individual observations, represents a promising and flexible extension of the popular minimum covariance determinant (MCD) estimator of the expectation and scatter matrix of mlutivariate data. In this work, a regularized version of the MWCD denoted as the minimum regularized weighted covariance determinant (MRWCD) estimator is proposed. At the same time, it is accompanied by an outlier detection procedure. The novel MRWCD estimator is able to outperform other available robust estimators in several simulation scenarios, especially in estimating the scatter matrix of contaminated high-dimensional data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10101 - Pure mathematics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Advances in Data Analysis and Classification

ISSN

1862-5347

e-ISSN

1862-5355

Svazek periodika

16

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

23

Strana od-do

977-999

Kód UT WoS článku

000705729800001

EID výsledku v databázi Scopus

2-s2.0-85116552764