A normal hierarchical model for random intervals

报告题目: A normal hierarchical model for random intervals 


报告人: Dan Ralescu 教授






摘要: Many statistical data are imprecise due to factors such as measurement errors, computation errors, and lack of information. In such cases, data are better represented by intervals rather than by single numbers. Existing methods for analyzing interval-valued data include regressions in the metric space of intervals and symbolic data analysis, the latter being proposed in a more general setting. However, there has been a lack of literature on the parametric modeling and distribution-based inferences for interval-valued data. In an attempt to  fill this gap, we extend the concept of normality for random sets  and propose a normal hierarchical model for random intervals. In addition, we develop a minimum contrast estimator (MCE) for the model parameters, which we show is both consistent and asymptotically normal. Simulation studies support our theoretical findings, and show very promising results. Finally, we successfully apply our model and MCE to a real dataset.


报告人简介:国际著名数学家,模糊数学的创始人之一,在模糊分析与模糊概率论等领域做出了重要贡献,目前主要从事不确定性数学理论及其应用的研究工作。现为美国辛辛那提大学(University of Cincinnati)数学系终身教授.








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