Minimum distance estimation in imprecise probability models

摘要

The present article considers estimating a parameter theta in an imprecise probability model ((P) over bar (theta))(theta is an element of circle dot). This model consists of coherent upper previsions (P) over bar (theta) which are given by Finite numbers of constraints on expectations. A minimum distance estimator is defined in this case and its asymptotic properties are investigated. It is shown that the minimum distance can be approximately calculated by discretizing the sample space. Finally. the estimator is applied in a simulation study and on a real data set.