The minimum weighted covariance determinant estimator revisited

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00522579" target="_blank" >RIV/67985807:_____/22:00522579 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/22:10472362

Výsledek na webu

<a href="https://dx.doi.org/10.1080/03610918.2020.1725818" target="_blank" >https://dx.doi.org/10.1080/03610918.2020.1725818</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03610918.2020.1725818" target="_blank" >10.1080/03610918.2020.1725818</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The minimum weighted covariance determinant estimator revisited

Popis výsledku v původním jazyce

This paper is devoted to robust estimation of parameters of multivariate data. It investigates the minimum weighted covariance determinant estimator, which is based on implicit weights assigned to individual observations and is highly resistant to the presence of outlying values (outliers). We propose alternative versions of the estimator, which can be computed by means of the same (approximate) algorithm. Based on numerical experiments, we recommend especially a version of the estimator based on minimizing the product of (only) several eigenvalues of the weighted covariance matrix of the data. This version is namely able to overcome the performance of several available estimators including MM-estimators on contaminated data. Another proposal with promising performance is a two-stage adaptive weighting scheme for the estimator.

Název v anglickém jazyce

The minimum weighted covariance determinant estimator revisited

Popis výsledku anglicky

This paper is devoted to robust estimation of parameters of multivariate data. It investigates the minimum weighted covariance determinant estimator, which is based on implicit weights assigned to individual observations and is highly resistant to the presence of outlying values (outliers). We propose alternative versions of the estimator, which can be computed by means of the same (approximate) algorithm. Based on numerical experiments, we recommend especially a version of the estimator based on minimizing the product of (only) several eigenvalues of the weighted covariance matrix of the data. This version is namely able to overcome the performance of several available estimators including MM-estimators on contaminated data. Another proposal with promising performance is a two-stage adaptive weighting scheme for the estimator.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA18-01137S" target="_blank" >GA18-01137S: Náhodné procesy regresních kvantilů v analýze finančního rizika</a><br>

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

Communications in Statistics - Simulation and Computation

ISSN

0361-0918

e-ISSN

1532-4141

Svazek periodika

51

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

3888-3900

Kód UT WoS článku

000513415700001

EID výsledku v databázi Scopus

2-s2.0-85079382560