imprProbEst-package: Minimum distance estimation in an imprecise probability model

In imprProbEst: Minimum distance estimation in an imprecise probability model

Description

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.

Details

Package:	imprProbEst
Type:	Package
Version:	1.0
Date:	2008-10-23
License:	LGPL-3
LazyLoad:	yes

library(imprProbEst

Note

R programming support was given by Matthias Kohl

Author(s)

Robert Hable

Maintainer: Robert Hable <[e-mail]>

References

Hable (2008) Data-Based Decisions under Complex Uncertainty, Ph.D. thesis, LMU Munich, in preparation

Walley, P. (1991) Statistical reasoning with imprecise probabilities. Chapman & Hall, London.

imprProbEst documentation built on May 2, 2019, 2:35 a.m.