Searching. Please wait…
1582
37
171
29406
4423
2606
347
392
Abstract: There exist multiple methods to detect outliers in multivariate data in the literature, but most of them require to estimate the covariance matrix. The higher the dimension, the more complex the estimation of the matrix becoming impossible in high dimensions. In order to avoid estimating this matrix, we propose a novel random projection-based procedure to detect outliers in Gaussian multivariate data. It consists in projecting the data in several one-dimensional subspaces where an appropriate univariate outlier detection method, similar to Tukey's method but with a threshold depending on the initial dimension and the sample size, is applied. The required number of projections is determined using sequential analysis. Simulated and real datasets illustrate the performance of the proposed method.
Fuente: TEST, 2021, 30 (4), 908 - 934
Publisher: Springer
Publication date: 01/12/2021
No. of pages: 27
Publication type: Article
DOI: 10.1007/s11749-020-00750-y
ISSN: 1133-0686,1863-8260
Spanish project: MTM2017-86061-C2-2-P
Publication Url: https://doi.org/10.1007/s11749-020-00750-y
SCOPUS
Citations
Google Scholar
Metrics
Read publication
PAULA NAVARRO ESTEBAN
JUAN ANTONIO CUESTA ALBERTOS
Back