The minimum weighted covariance determinant estimator for high-dimensional data
The result's identifiers
Result code in 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>
Alternative codes found
RIV/67985807:_____/22:00546601
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
The minimum weighted covariance determinant estimator for high-dimensional data
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10101 - Pure mathematics
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Advances in Data Analysis and Classification
ISSN
1862-5347
e-ISSN
1862-5355
Volume of the periodical
16
Issue of the periodical within the volume
4
Country of publishing house
DE - GERMANY
Number of pages
23
Pages from-to
977-999
UT code for WoS article
000705729800001
EID of the result in the Scopus database
2-s2.0-85116552764