Robust minimum information loss estimation
Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample - typically of covariance or crosss-pectrum matrices - with the subsample chosen to minimize a function of its average. In one case this function is the Kullbac...
Guardado en:
| Publicado: |
2013
|
|---|---|
| Materias: | |
| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v65_n_p98_Lind http://hdl.handle.net/20.500.12110/paper_01679473_v65_n_p98_Lind |
| Aporte de: |
| id |
paper:paper_01679473_v65_n_p98_Lind |
|---|---|
| record_format |
dspace |
| spelling |
paper:paper_01679473_v65_n_p98_Lind2023-06-08T15:17:11Z Robust minimum information loss estimation Breakdown Covariance Cross-spectrum matrix Electroencephalogram recording Genetic algorithm Minimum covariance determinant Minimum information loss determinant estimate Spectrum Trimmed minimum information loss estimate Covariance matrix Electroencephalography Genetic algorithms Spectroscopy Breakdown Covariance Cross spectra Minimum covariance determinant Minimum information loss Spectrum Matrix algebra Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample - typically of covariance or crosss-pectrum matrices - with the subsample chosen to minimize a function of its average. In one case this function is the Kullback-Leibler discrimination information loss incurred when the subsample is summarized by its average; in the other it is the determinant, subject to a certain side condition. For each, the authors give an efficient computing algorithm, and show that the estimator has, asymptotically, the maximum possible breakdown point. The main motivation is the need for efficient and robust estimation of cross-spectrum matrices, and they present a case study in which the data points originate as multichannel electroencephalogram recordings but are then summarized by the corresponding sample cross-spectrum matrices. © 2012 Elsevier B.V. All rights reserved. 2013 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v65_n_p98_Lind http://hdl.handle.net/20.500.12110/paper_01679473_v65_n_p98_Lind |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Breakdown Covariance Cross-spectrum matrix Electroencephalogram recording Genetic algorithm Minimum covariance determinant Minimum information loss determinant estimate Spectrum Trimmed minimum information loss estimate Covariance matrix Electroencephalography Genetic algorithms Spectroscopy Breakdown Covariance Cross spectra Minimum covariance determinant Minimum information loss Spectrum Matrix algebra |
| spellingShingle |
Breakdown Covariance Cross-spectrum matrix Electroencephalogram recording Genetic algorithm Minimum covariance determinant Minimum information loss determinant estimate Spectrum Trimmed minimum information loss estimate Covariance matrix Electroencephalography Genetic algorithms Spectroscopy Breakdown Covariance Cross spectra Minimum covariance determinant Minimum information loss Spectrum Matrix algebra Robust minimum information loss estimation |
| topic_facet |
Breakdown Covariance Cross-spectrum matrix Electroencephalogram recording Genetic algorithm Minimum covariance determinant Minimum information loss determinant estimate Spectrum Trimmed minimum information loss estimate Covariance matrix Electroencephalography Genetic algorithms Spectroscopy Breakdown Covariance Cross spectra Minimum covariance determinant Minimum information loss Spectrum Matrix algebra |
| description |
Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample - typically of covariance or crosss-pectrum matrices - with the subsample chosen to minimize a function of its average. In one case this function is the Kullback-Leibler discrimination information loss incurred when the subsample is summarized by its average; in the other it is the determinant, subject to a certain side condition. For each, the authors give an efficient computing algorithm, and show that the estimator has, asymptotically, the maximum possible breakdown point. The main motivation is the need for efficient and robust estimation of cross-spectrum matrices, and they present a case study in which the data points originate as multichannel electroencephalogram recordings but are then summarized by the corresponding sample cross-spectrum matrices. © 2012 Elsevier B.V. All rights reserved. |
| title |
Robust minimum information loss estimation |
| title_short |
Robust minimum information loss estimation |
| title_full |
Robust minimum information loss estimation |
| title_fullStr |
Robust minimum information loss estimation |
| title_full_unstemmed |
Robust minimum information loss estimation |
| title_sort |
robust minimum information loss estimation |
| publishDate |
2013 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v65_n_p98_Lind http://hdl.handle.net/20.500.12110/paper_01679473_v65_n_p98_Lind |
| _version_ |
1768542264506712064 |