A minimum distance estimator in an imprecise probability model - Computational aspects and applications |
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Authors: | Robert Hable |
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Institution: | University of Bayreuth, Department of Mathematics, 95440 Bayreuth, Germany |
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Abstract: | The article considers estimating a parameter θ in an imprecise probability model which consists of coherent upper previsions . 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. |
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Keywords: | Imprecise probability Coherent lower prevision Minimum distance estimator Empirical measure R Project for statistical computing |
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