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

作者：Hable Robert^*

来源：International Journal of Approximate Reasoning, 2010, 51(9): 1114-1128.

DOI：10.1016/j.ijar.2010.08.003

• Imprecise probability
• Coherent lower prevision
• Minimum distance estimator
• Empirical measure
• R Project for statistical computing

The article considers estimating a parameter theta in an imprecise probability model ((P) over bar (theta))(theta is an element of theta) which consists of coherent upper previsions (P) over bar (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.

• 出版日期2010-11