Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149407 | Journal of Statistical Planning and Inference | 2010 | 19 Pages |
Abstract
The present article considers estimating a parameter θθ in an imprecise probability model (P¯θ)θ∈Θ. This model consists of coherent upper previsions P¯θ 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.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Robert Hable,