Propagation of outliers in multivariate data

We investigate the performance of robust estimates of multivariate location under nonstandard data contamination models such as componentwise outliers (i.e., contamination in each variable is independent from the other variables). This model brings up a possible new source of statistical error that...

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Detalles Bibliográficos
Publicado: 2009
Materias:
Breakdown point
Contamination model
Independent contamination
Influence function
Robustness
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00905364_v37_n1_p311_Alqallaf
http://hdl.handle.net/20.500.12110/paper_00905364_v37_n1_p311_Alqallaf
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_00905364_v37_n1_p311_Alqallaf
record_format dspace
spelling paper:paper_00905364_v37_n1_p311_Alqallaf2023-06-08T15:07:50Z Propagation of outliers in multivariate data Breakdown point Contamination model Independent contamination Influence function Robustness We investigate the performance of robust estimates of multivariate location under nonstandard data contamination models such as componentwise outliers (i.e., contamination in each variable is independent from the other variables). This model brings up a possible new source of statistical error that we call "propagation of outliers." This source of error is unusual in the sense that it is generated by the data processing itself and takes place after the data has been collected. We define and derive the influence function of robust multivariate location estimates under flexible contamination models and use it to investigate the effect of propagation of outliers. Furthermore, we show that standard high-breakdown affine equivariant estimators propagate outliers and therefore show poor breakdown behavior under componentwise contamination when the dimension d is high. © Institute of Mathematical Statistics, 2009. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00905364_v37_n1_p311_Alqallaf http://hdl.handle.net/20.500.12110/paper_00905364_v37_n1_p311_Alqallaf
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 point
Contamination model
Independent contamination
Influence function
Robustness
spellingShingle Breakdown point
Contamination model
Independent contamination
Influence function
Robustness
Propagation of outliers in multivariate data
topic_facet Breakdown point
Contamination model
Independent contamination
Influence function
Robustness
description We investigate the performance of robust estimates of multivariate location under nonstandard data contamination models such as componentwise outliers (i.e., contamination in each variable is independent from the other variables). This model brings up a possible new source of statistical error that we call "propagation of outliers." This source of error is unusual in the sense that it is generated by the data processing itself and takes place after the data has been collected. We define and derive the influence function of robust multivariate location estimates under flexible contamination models and use it to investigate the effect of propagation of outliers. Furthermore, we show that standard high-breakdown affine equivariant estimators propagate outliers and therefore show poor breakdown behavior under componentwise contamination when the dimension d is high. © Institute of Mathematical Statistics, 2009.
title Propagation of outliers in multivariate data
title_short Propagation of outliers in multivariate data
title_full Propagation of outliers in multivariate data
title_fullStr Propagation of outliers in multivariate data
title_full_unstemmed Propagation of outliers in multivariate data
title_sort propagation of outliers in multivariate data
publishDate 2009
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00905364_v37_n1_p311_Alqallaf
http://hdl.handle.net/20.500.12110/paper_00905364_v37_n1_p311_Alqallaf
_version_ 1768541698899574784

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