A minimum distance estimator in an imprecise probability model -- Computational aspects and applications

Authors:
Robert Hable
Affiliations:
University of Bayreuth, Department of Mathematics, 95440 Bayreuth, Germany
Venue:
International Journal of Approximate Reasoning
Year:
2010

Citing 2
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Abstract

The article considers estimating a parameter @q in an imprecise probability model (P@?"@q)"@q"@?"@Q which consists of coherent upper previsions P@?"@q. After the definition of a minimum distance estimator in this setup and a summarization of its main properties, the focus lies on applications. It is shown that approximate minimum distances on the discretized sample space can be calculated by linear programming. After a discussion of some computational aspects, the estimator is applied in a simulation study consisting of two different models. Finally, the estimator is applied on a real data set in a linear regression model.