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

The present article considers estimating a parameter θ in an imprecise probability model (Pθ)θ∈Θ which consists of coherent upper previsions Pθ . 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.

• Type: Journal
• Year: 2010
• Where: IJAR
• Authors: Robert Hable
• Keywords: IJAR 2010, Imprecise Probability Model, Minimum Distance, Minimum Distance Estimator