imprProbEst
imprProbEst: Minimum distance estimation in an imprecise probability model , 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.
(Source: http://cran.r-project.org/web/packages)
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
Sorted by year (- Benavoli, Alessio: Belief function and multivalued mapping robustness in statistical estimation (2014)
- Škulj, D.; Hable, R.: Coefficients of ergodicity for Markov chains with uncertain parameters (2013)
- Hable, Robert: Minimum distance estimation in imprecise probability models (2010)
- Hable, Robert: A minimum distance estimator in an imprecise probability model -- computational aspects and applications (2010)
- Hable, Robert: Data-based decisions under complex uncertainty. (2009)