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 |

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
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