Robust prediction limits based on M-estimators
F. Giummolè and
L. Ventura
Statistics & Probability Letters, 2006, vol. 76, issue 16, 1735-1740
Abstract:
We discuss a robust solution to the problem of prediction. Extending Barndorff-Nielsen and Cox [1996. Prediction and asymptotics. Bernoulli 2, 319-340] and Vidoni [1998. A note on modified estimative prediction limits and distributions. Biometrika 85, 949-953], we propose improved prediction limits based on M-estimators. To compute them, the expressions of the bias and variance of an M-estimator are required. In view of this, 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 can be successfully used in a parametric setting.
Keywords: Bias; Influence; function; Prediction; Robustness; Scale-regression; model (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(06)00129-5
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:76:y:2006:i:16:p:1735-1740
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
Access Statistics for this article
Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul
More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().