ISIPTA'09 - SIXTH INTERNATIONAL SYMPOSIUM ON
IMPRECISE PROBABILITY: THEORIES AND APPLICATIONS

Durham University, Department of Mathematical Sciences
Durham, United Kingdom
Tuesday 14 to Saturday 18 July 2009

ELECTRONIC PROCEEDINGS

Robert Hable

A Minimum Distance Estimator in an Imprecise Probability Model - Computational Aspects and Applications

Abstract

The present article considers estimating a parameter $\theta$ in an imprecise probability model $(\overline{P}_{\theta})_{\theta\in\Theta}$ which consists of coherent upper previsions $\overline{P}_{\theta}$. 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.

Keywords. Imprecise probabilities, coherent lower previsions, minimum distance estimator, empirical measure, R Project for Statistical Computing

Paper Download

The paper is availabe in the following formats:

Plenary talk : Press here to get the file of the presentation.

Authors addresses:

Department of Mathematics
University of Bayreuth
D-95440 Bayreuth
Germany

E-mail addresses:

Robert Hable Robert.Hable@uni-bayreuth.de

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