0546694 20230418204308.920211015235959.9 85116552764 000705729800001 10.1007/s11634-021-00471-6 23 s. P cav_un_epca*03616981862-5347164 (2022)977-999Springer High-dimensional data Regularization Robust estimation Implicit weighting Scatter matrix cav_un_auth*0345793 Kalina Jan UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0387590 Tichavský J. CZ http://library.utia.cas.cz/separaty/2021/SI/kalina-0546694.pdf https://link.springer.com/article/10.1007/s11634-021-00471-6 GA21-05325S GA ČR cav_un_auth*0409039 GA19-05704S GA ČR CZ cav_un_auth*0375756 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. WOS BA 10000 10100 10101 2023 RVO:67985556 http://hdl.handle.net/11104/0323102 S Article Statistics Probability C STATISTICS&PROBABILITY 1.9 0.2 6.2 0.00186 0.943 900 0.853 1.500 45 Q2 57.2 Q2 Q2 0.77 Q1 57.2 2022 85116552764 SCOPUS PUBMED 000705729800001 WOS cav_un_epca*0361698 Advances in Data Analysis and Classification 1862-5347 1862-5355 Roč. 16 č. 4 2022 977 999 Springer