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Robust estimators under the Imprecise Dirichlet Model

Hutter, Marcus

Description

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...[Show more]

dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-09-02T02:15:29Z
dc.date.available2015-09-02T02:15:29Z
dc.identifier.isbn1-894145-17-8
dc.identifier.urihttp://hdl.handle.net/1885/15080
dc.description.abstractWalley'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.
dc.publisherCarleton Scientific
dc.relation.ispartofProceedings of the Third International Symposium on Imprecise Probabilities and Their Applications, Lugano, Switzerland, July 14-17, 2003
dc.subjectImprecise Dirichlet Model
dc.subjectconfidence interval estimates
dc.subjectmutual information
dc.titleRobust estimators under the Imprecise Dirichlet Model
dc.typeConference paper
local.identifier.citationvolume18
dc.date.issued2003
local.type.statusPublished Version
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.bibliographicCitation.startpage274
local.bibliographicCitation.lastpage289
CollectionsANU Research Publications

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