Publication
Title
DetMCD in a calibration framework
Author
Abstract
The minimum covariance determinant (MCD) method is a robust estimator of multivariate location and scatter (Rousseeuw (1984)). Computing the exact MCD is very hard, so in practice one resorts to approximate algorithms. Most often the FASTMCD algorithm of Rousseeuw and Van Driessen (1999) is used. The FASTMCD algorithm is affine equivariant but not permutation invariant. Recently a deterministic algorithm, denoted as DetMCD, is developed which does not use random subsets and which is much faster (Hubert et al. (2010)). In this paper DetMCD is illustrated in a calibration framework. We focus on robust principal component regression and partial least squares regression, two very popular regression techniques for collinear data. We also apply DetMCD on data with missing elements after plugging it into the M-RPCR technique of Serneels and Verdonck (2009).
Language
English
Source (journal)
COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS
Source (book)
19th International Conference on Computational Statistics, (COMPSTAT'2010), AUG 22-27, 2010, Paris, FRANCE
Publication
Heidelberg : Physica-verlag gmbh & co, 2010
ISBN
978-3-7908-2603-6
Volume/pages
(2010), p. 589-596
ISI
000395720500061
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 02.08.2018
Last edited 24.07.2021
To cite this reference