Influence functions of two families of robust estimators under proportional scatter matrices
In this paper, under a proportional model, two families of robust estimates for the proportionality constants, the common principal axes and their size are discussed. The first approach is obtained by plugging robust scatter matrices on the maximum likelihood equations for normal data. A projection-...
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todo:paper_16182510_v15_n3_p295_Boente2023-10-03T16:28:28Z Influence functions of two families of robust estimators under proportional scatter matrices Boente, G. Critchley, F. Orellana, L. Partial influence curves Projection-pursuit Proportional scatter models Robust estimation Robust scatter matrices In this paper, under a proportional model, two families of robust estimates for the proportionality constants, the common principal axes and their size are discussed. The first approach is obtained by plugging robust scatter matrices on the maximum likelihood equations for normal data. A projection- pursuit and a modified projection-pursuit approach, adapted to the proportional setting, are also considered. For all families of estimates, partial influence functions are obtained and asymptotic variances are derived from them. The performance of the estimates is compared through a Monte Carlo study. © 2006 Springer-Verlag. Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_16182510_v15_n3_p295_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 |
Partial influence curves Projection-pursuit Proportional scatter models Robust estimation Robust scatter matrices |
spellingShingle |
Partial influence curves Projection-pursuit Proportional scatter models Robust estimation Robust scatter matrices Boente, G. Critchley, F. Orellana, L. Influence functions of two families of robust estimators under proportional scatter matrices |
topic_facet |
Partial influence curves Projection-pursuit Proportional scatter models Robust estimation Robust scatter matrices |
description |
In this paper, under a proportional model, two families of robust estimates for the proportionality constants, the common principal axes and their size are discussed. The first approach is obtained by plugging robust scatter matrices on the maximum likelihood equations for normal data. A projection- pursuit and a modified projection-pursuit approach, adapted to the proportional setting, are also considered. For all families of estimates, partial influence functions are obtained and asymptotic variances are derived from them. The performance of the estimates is compared through a Monte Carlo study. © 2006 Springer-Verlag. |
format |
JOUR |
author |
Boente, G. Critchley, F. Orellana, L. |
author_facet |
Boente, G. Critchley, F. Orellana, L. |
author_sort |
Boente, G. |
title |
Influence functions of two families of robust estimators under proportional scatter matrices |
title_short |
Influence functions of two families of robust estimators under proportional scatter matrices |
title_full |
Influence functions of two families of robust estimators under proportional scatter matrices |
title_fullStr |
Influence functions of two families of robust estimators under proportional scatter matrices |
title_full_unstemmed |
Influence functions of two families of robust estimators under proportional scatter matrices |
title_sort |
influence functions of two families of robust estimators under proportional scatter matrices |
url |
http://hdl.handle.net/20.500.12110/paper_16182510_v15_n3_p295_Boente |
work_keys_str_mv |
AT boenteg influencefunctionsoftwofamiliesofrobustestimatorsunderproportionalscattermatrices AT critchleyf influencefunctionsoftwofamiliesofrobustestimatorsunderproportionalscattermatrices AT orellanal influencefunctionsoftwofamiliesofrobustestimatorsunderproportionalscattermatrices |
_version_ |
1782026954276339712 |