Robust Estimators under the Imprecise Dirichlet Model
Keywords: Imprecise Dirichlet Model;
exact, conservative, approximate, robust, credible interval estimates;
entropy; mutual information.
Abstract: Walley's Imprecise Dirichlet Model (IDM) for categorical data
overcomes several fundamental problems which other approaches to
uncertainty suffer from. Yet, to be useful in practice, one needs
efficient ways for computing the imprecise=robust sets or
intervals. The main objective of this work is to derive exact,
conservative, and approximate, robust and credible interval
estimates under the IDM for a large class of statistical
estimators, including the entropy and mutual information.
BibTeX Entry
@InProceedings{Hutter:03idm,
author = "M. Hutter",
number = "IDSIA-03-03",
title = "Robust Estimators under the {I}mprecise {D}irichlet {M}odel",
booktitle = "Proceedings of the 3nd International Symposium on
Imprecise Probabilities and Their Application ({ISIPTA-2003})",
editor = "J.-M. Bernard and T. Seidenfeld and M. Zaffalon",
publisher = "Carleton Scientific",
series = "Proceedings in Informatics",
volume = "18",
address = "Canada",
year = "2003",
pages = "274--289",
http = "http://www.hutter1.net/ai/idm.htm",
url = "http://arxiv.org/abs/math.PR/0305121",
ftp = "ftp://ftp.idsia.ch/pub/techrep/IDSIA-03-03.ps.gz",
keywords = "Imprecise Dirichlet Model; exact, conservative, approximate,
robust, confidence interval estimates; entropy; mutual information.",
abstract = "Walley's Imprecise Dirichlet Model (IDM) for categorical data
overcomes several fundamental problems which other approaches to
uncertainty suffer from. Yet, to be useful in practice, one needs
efficient ways for computing the imprecise=robust sets or
intervals. The main objective of this work is to derive exact,
conservative, and approximate, robust and credible interval
estimates under the IDM for a large class of statistical
estimators, including the entropy and mutual information.",
}