The minimum weighted covariance determinant estimator revisited

0522579 - ÚI 2023 RIV US eng J - Journal Article
Kalina, Jan
The minimum weighted covariance determinant estimator revisited.
Communications in Statistics - Simulation and Computation. Roč. 51, č. 7 (2022), s. 3888-3900. ISSN 0361-0918. E-ISSN 1532-4141
Grant - others:GA ČR(CZ) GA18-01137S
Institutional support: RVO:67985807
Keywords : Consistency factor * Implicit weighting * Multivariate data * Robustness * Simulations
OECD category: Statistics and probability
Impact factor: 0.9, year: 2022
Method of publishing: Limited access
https://dx.doi.org/10.1080/03610918.2020.1725818

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.
Permanent Link: http://hdl.handle.net/11104/0307053