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Higher-order robustness

Contributors

La Vecchia, Davide; Ronchetti, Elvezio; Trojani, Fabio

Presented at

2nd International Workshop of the ERCIM Working Group on Computing & Statistics, Limassol (Cyprus), 29-31 October 2009

Publication date

2009

Abstract

The higher–order robustness for M–estimators is introduced and defined. The conditions needed to ensure higher stability of the asymptotic bias are provided by refining the Von Mises bias expansion. Admissible M–estimators featuring second–order robustness are thus introduced. Then, a saddle-point argument is applied in order to approximate the finite sample distribution of second–order robust M–estimators. The link between the stability of this approximation and the second–order robustness is explored. Monte Carlo simulation provides evidence that second–order robust M–estimators perform better than the MLE and Huber–type estimators, even in moderate to small sample sizes and/or for large amounts of contamination.

Keywords

  • Von mises expansion
  • M-estimator
  • Robustness
  • Saddle point
  • Generalized extreme value distribution

UNIGE affiliation entities

Citation (ISO format)

LA VECCHIA, Davide, RONCHETTI, Elvezio, TROJANI, Fabio. Higher-order robustness. In: 2nd International Workshop of the ERCIM Working Group on Computing & Statistics. Limassol (Cyprus). 2009.

Main files (1)

Presentation

Identifiers

  • PID : unige:75154

Additional URL for this publication

http://www.cfe-csda.org/ercim09/BoACFEERCIM09.pdf
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655downloads

Technical informations

Creation

13/09/2015 12:43:00

First validation

13/09/2015 12:43:00

Update

18/02/2026 08:24:09

Status update

18/02/2026 08:24:09

Last indexation

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