A note on the uniform asymptotic normality of location M-estimates

Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Berrendero, José R. [verfasserIn] Zamar, Ruben H.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2006

Schlagwörter:	M-estimates U-statistics GS-estimates Robust estimation

Anmerkung:	© Springer-Verlag 2005

Übergeordnetes Werk:	Enthalten in: Metrika - Berlin : Springer, 1958, 63(2006), 1 vom: 10. Jan., Seite 55-69
Übergeordnetes Werk:	volume:63 ; year:2006 ; number:1 ; day:10 ; month:01 ; pages:55-69

Links:	Volltext

DOI / URN:	10.1007/s00184-005-0006-y

Katalog-ID:	SPR001551833

Internformat


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100	1		\|a Berrendero, José R. \|e verfasserin \|4 aut
245	1	2	\|a A note on the uniform asymptotic normality of location M-estimates
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520			\|a Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004)
650		4	\|a M-estimates \|7 (dpeaa)DE-He213
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650		4	\|a GS-estimates \|7 (dpeaa)DE-He213
650		4	\|a Robust estimation \|7 (dpeaa)DE-He213
700	1		\|a Zamar, Ruben H. \|4 aut
773	0	8	\|i Enthalten in \|t Metrika \|d Berlin : Springer, 1958 \|g 63(2006), 1 vom: 10. Jan., Seite 55-69 \|w (DE-627)254630952 \|w (DE-600)1462149-6 \|x 1435-926X \|7 nnns
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912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
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912			\|a GBV_ILN_161
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912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
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912			\|a GBV_ILN_370
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912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
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912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4012
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publishDate	2006
allfields	10.1007/s00184-005-0006-y doi (DE-627)SPR001551833 (SPR)s00184-005-0006-y-e DE-627 ger DE-627 rakwb eng Berrendero, José R. verfasserin aut A note on the uniform asymptotic normality of location M-estimates 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004) M-estimates (dpeaa)DE-He213 U-statistics (dpeaa)DE-He213 GS-estimates (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Zamar, Ruben H. aut Enthalten in Metrika Berlin : Springer, 1958 63(2006), 1 vom: 10. Jan., Seite 55-69 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:63 year:2006 number:1 day:10 month:01 pages:55-69 https://dx.doi.org/10.1007/s00184-005-0006-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 63 2006 1 10 01 55-69
spelling	10.1007/s00184-005-0006-y doi (DE-627)SPR001551833 (SPR)s00184-005-0006-y-e DE-627 ger DE-627 rakwb eng Berrendero, José R. verfasserin aut A note on the uniform asymptotic normality of location M-estimates 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004) M-estimates (dpeaa)DE-He213 U-statistics (dpeaa)DE-He213 GS-estimates (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Zamar, Ruben H. aut Enthalten in Metrika Berlin : Springer, 1958 63(2006), 1 vom: 10. Jan., Seite 55-69 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:63 year:2006 number:1 day:10 month:01 pages:55-69 https://dx.doi.org/10.1007/s00184-005-0006-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 63 2006 1 10 01 55-69
allfields_unstemmed	10.1007/s00184-005-0006-y doi (DE-627)SPR001551833 (SPR)s00184-005-0006-y-e DE-627 ger DE-627 rakwb eng Berrendero, José R. verfasserin aut A note on the uniform asymptotic normality of location M-estimates 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004) M-estimates (dpeaa)DE-He213 U-statistics (dpeaa)DE-He213 GS-estimates (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Zamar, Ruben H. aut Enthalten in Metrika Berlin : Springer, 1958 63(2006), 1 vom: 10. Jan., Seite 55-69 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:63 year:2006 number:1 day:10 month:01 pages:55-69 https://dx.doi.org/10.1007/s00184-005-0006-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 63 2006 1 10 01 55-69
allfieldsGer	10.1007/s00184-005-0006-y doi (DE-627)SPR001551833 (SPR)s00184-005-0006-y-e DE-627 ger DE-627 rakwb eng Berrendero, José R. verfasserin aut A note on the uniform asymptotic normality of location M-estimates 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004) M-estimates (dpeaa)DE-He213 U-statistics (dpeaa)DE-He213 GS-estimates (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Zamar, Ruben H. aut Enthalten in Metrika Berlin : Springer, 1958 63(2006), 1 vom: 10. Jan., Seite 55-69 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:63 year:2006 number:1 day:10 month:01 pages:55-69 https://dx.doi.org/10.1007/s00184-005-0006-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 63 2006 1 10 01 55-69
allfieldsSound	10.1007/s00184-005-0006-y doi (DE-627)SPR001551833 (SPR)s00184-005-0006-y-e DE-627 ger DE-627 rakwb eng Berrendero, José R. verfasserin aut A note on the uniform asymptotic normality of location M-estimates 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2005 Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004) M-estimates (dpeaa)DE-He213 U-statistics (dpeaa)DE-He213 GS-estimates (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Zamar, Ruben H. aut Enthalten in Metrika Berlin : Springer, 1958 63(2006), 1 vom: 10. Jan., Seite 55-69 (DE-627)254630952 (DE-600)1462149-6 1435-926X nnns volume:63 year:2006 number:1 day:10 month:01 pages:55-69 https://dx.doi.org/10.1007/s00184-005-0006-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 63 2006 1 10 01 55-69
language	English
source	Enthalten in Metrika 63(2006), 1 vom: 10. Jan., Seite 55-69 volume:63 year:2006 number:1 day:10 month:01 pages:55-69
sourceStr	Enthalten in Metrika 63(2006), 1 vom: 10. Jan., Seite 55-69 volume:63 year:2006 number:1 day:10 month:01 pages:55-69
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	M-estimates U-statistics GS-estimates Robust estimation
isfreeaccess_bool	false
container_title	Metrika
authorswithroles_txt_mv	Berrendero, José R. @@aut@@ Zamar, Ruben H. @@aut@@
publishDateDaySort_date	2006-01-10T00:00:00Z
hierarchy_top_id	254630952
id	SPR001551833
language_de	englisch
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author	Berrendero, José R.
spellingShingle	Berrendero, José R. misc M-estimates misc U-statistics misc GS-estimates misc Robust estimation A note on the uniform asymptotic normality of location M-estimates
authorStr	Berrendero, José R.
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topic_title	A note on the uniform asymptotic normality of location M-estimates M-estimates (dpeaa)DE-He213 U-statistics (dpeaa)DE-He213 GS-estimates (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213
topic	misc M-estimates misc U-statistics misc GS-estimates misc Robust estimation
topic_unstemmed	misc M-estimates misc U-statistics misc GS-estimates misc Robust estimation
topic_browse	misc M-estimates misc U-statistics misc GS-estimates misc Robust estimation
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title	A note on the uniform asymptotic normality of location M-estimates
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title_full	A note on the uniform asymptotic normality of location M-estimates
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author_browse	Berrendero, José R. Zamar, Ruben H.
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title_sort	note on the uniform asymptotic normality of location m-estimates
title_auth	A note on the uniform asymptotic normality of location M-estimates
abstract	Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004) © Springer-Verlag 2005
abstractGer	Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004) © Springer-Verlag 2005
abstract_unstemmed	Abstract In the robustness framework, the parametric model underlying the data is usually embedded in a neighborhood of other plausible distributions. Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. These results represent an improvement over those obtained recently by Salibian-Barrera and Zamar (2004) © Springer-Verlag 2005
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title_short	A note on the uniform asymptotic normality of location M-estimates
url	https://dx.doi.org/10.1007/s00184-005-0006-y
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doi_str	10.1007/s00184-005-0006-y
up_date	2024-07-03T23:14:58.245Z
_version_	1803601574102040577
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Accordingly, the asymptotic properties of robust estimates should be uniform over the whole set of possible models. In this paper, we study location M-estimates calculated with a previous generalized S-scale and show that, under some regularity conditions, they are uniformly asymptotically normal over contamination neighborhoods of known size. There is a trade off between the size of the neighborhood and the breakdown point of the GS-scale, but it is possible to adjust the estimates so that they have 50% breakdown point whereas the uniform asymptotic normality is ensured over neighborhoods that contain up to 25% of contamination. Alternatively, both the breakdown point and the size of the neighborhood could be chosen to be 38%. 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A note on the uniform asymptotic normality of location M-estimates

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