Robust multivariate tolerance regions: Influence function and Monte Carlo study
In this article we define a class of multivariate tolerance regions that turn out to be more resistant than the classical ones to outliers. The tolerance factors are numerically evaluated under the central model, and the sensitivity to deviations from the normal distribution for moderate samples is...
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2008
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Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00401706_v50_n4_p487_Boente http://hdl.handle.net/20.500.12110/paper_00401706_v50_n4_p487_Boente |
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paper:paper_00401706_v50_n4_p487_Boente2023-06-08T15:03:47Z Robust multivariate tolerance regions: Influence function and Monte Carlo study Coverage probability Donoho-Stahel estimator Multivariate normal distribution Robustness Tolerance region Coverage probability Donoho-Stahel estimator Multivariate normal distribution Robustness Tolerance region Monte Carlo methods Normal distribution In this article we define a class of multivariate tolerance regions that turn out to be more resistant than the classical ones to outliers. The tolerance factors are numerically evaluated under the central model, and the sensitivity to deviations from the normal distribution for moderate samples is studied through a Monte Carlo study. Moreover, the influence function of the coverage probability allows us to compare the sensitivity of different proposals to anomalous data. Finally, real data examples are discussed. © 2008 American Statistical Association and the American Society for Quality. 2008 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00401706_v50_n4_p487_Boente http://hdl.handle.net/20.500.12110/paper_00401706_v50_n4_p487_Boente |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Coverage probability Donoho-Stahel estimator Multivariate normal distribution Robustness Tolerance region Coverage probability Donoho-Stahel estimator Multivariate normal distribution Robustness Tolerance region Monte Carlo methods Normal distribution |
spellingShingle |
Coverage probability Donoho-Stahel estimator Multivariate normal distribution Robustness Tolerance region Coverage probability Donoho-Stahel estimator Multivariate normal distribution Robustness Tolerance region Monte Carlo methods Normal distribution Robust multivariate tolerance regions: Influence function and Monte Carlo study |
topic_facet |
Coverage probability Donoho-Stahel estimator Multivariate normal distribution Robustness Tolerance region Coverage probability Donoho-Stahel estimator Multivariate normal distribution Robustness Tolerance region Monte Carlo methods Normal distribution |
description |
In this article we define a class of multivariate tolerance regions that turn out to be more resistant than the classical ones to outliers. The tolerance factors are numerically evaluated under the central model, and the sensitivity to deviations from the normal distribution for moderate samples is studied through a Monte Carlo study. Moreover, the influence function of the coverage probability allows us to compare the sensitivity of different proposals to anomalous data. Finally, real data examples are discussed. © 2008 American Statistical Association and the American Society for Quality. |
title |
Robust multivariate tolerance regions: Influence function and Monte Carlo study |
title_short |
Robust multivariate tolerance regions: Influence function and Monte Carlo study |
title_full |
Robust multivariate tolerance regions: Influence function and Monte Carlo study |
title_fullStr |
Robust multivariate tolerance regions: Influence function and Monte Carlo study |
title_full_unstemmed |
Robust multivariate tolerance regions: Influence function and Monte Carlo study |
title_sort |
robust multivariate tolerance regions: influence function and monte carlo study |
publishDate |
2008 |
url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00401706_v50_n4_p487_Boente http://hdl.handle.net/20.500.12110/paper_00401706_v50_n4_p487_Boente |
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1768543647359303680 |