imprProbEst: Minimum distance estimation in an imprecise probability model

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A minimum distance estimator is calculated for an imprecise probability model. The imprecise probability model consists of upper coherent previsions whose credal sets are given by a finite number of constraints on the expectations. The parameter set is finite. The estimator chooses that parameter such that the empirical measure lies next to the corresponding credal set with respect to the total variation norm.

Author
Robert Hable
Date of publication
2010-05-07 16:10:30
Maintainer
Robert Hable <Robert.Hable@uni-bayreuth.de>
License
LGPL-3
Version
1.0.1

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Man pages

ArgMinDist
A Minimum Distance Estimation
imprProbEst-package
Minimum distance estimation in an imprecise probability model

Files in this package

imprProbEst
imprProbEst/DESCRIPTION
imprProbEst/inst
imprProbEst/inst/CITATION
imprProbEst/man
imprProbEst/man/ArgMinDist.Rd
imprProbEst/man/imprProbEst-package.Rd
imprProbEst/NAMESPACE
imprProbEst/R
imprProbEst/R/ArgMinDist.R
imprProbEst/R/BuildBounds.R
imprProbEst/R/BuildBounds1.R
imprProbEst/R/BuildBounds2.R
imprProbEst/R/BuildMatrix.R
imprProbEst/R/BuildMatrix1.R
imprProbEst/R/BuildMatrix2.R
imprProbEst/R/BuildOptVec.R
imprProbEst/R/BuildSupportingNodes.R
imprProbEst/R/DiscretizeImpreciseModel.R
imprProbEst/R/FctReducedNodevalues.R
imprProbEst/R/fevaluation.R
imprProbEst/R/TotalVar.R