In this paper we discuss a robust solution to the problem of prediction. Following Barndorff-Nielsen and Cox (1996) and Vidoni (1998), we propose improved prediction limits based on M-estimators instead of maximum likelihood estimators. To compute these robust prediction limits, the expressions of the bias and variance of an M-estimator are required. Here a general asymptotic approximation for the bias of an M-estimator is derived. Moreover, by means of comparative studies in the context of affine transformation models, we show that the proposed robust procedure for prediction behaves in a similar manner to the classical one when the model is correctly specified, but it is designed to be stable in a neighborhood of the model.

Robust prediction limits based on M-estimators

Ventura, Laura
2004

Abstract

In this paper we discuss a robust solution to the problem of prediction. Following Barndorff-Nielsen and Cox (1996) and Vidoni (1998), we propose improved prediction limits based on M-estimators instead of maximum likelihood estimators. To compute these robust prediction limits, the expressions of the bias and variance of an M-estimator are required. Here a general asymptotic approximation for the bias of an M-estimator is derived. Moreover, by means of comparative studies in the context of affine transformation models, we show that the proposed robust procedure for prediction behaves in a similar manner to the classical one when the model is correctly specified, but it is designed to be stable in a neighborhood of the model.
File in questo prodotto:
File Dimensione Formato  
2004_8.pdf

accesso aperto

Licenza: Non specificato
Dimensione 899.87 kB
Formato Adobe PDF
899.87 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3442326
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact