A normal hierarchical model for random intervals

Title: A normal hierarchical model for random intervals 


SpeakerDan RalescuUniversity of Cincinnati


Date:  16:00-17:00 pm, July 9, 2014


Place: Conference Room B304, Department of Mathematical Sciences


Abstract: 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.


ContactBaoding Liu