报告题目： Statistical Inference for Change Regions in Spatial Models

报告摘要： Non-stationary spatial models are widely applicable in diverse disciplines, ranging from bio-medical sciences to geophysical  studies. In many of these applications, testing for structural changes in the trend constitutes an important issue. To achieve this goal, a novel statistics based on a discrepancy measure over small blocks is proposed in this paper. Such a measure can be used not only to construct tests for structural breaks, but also to identify change boundaries of the breaks.  Asymptotic properties and limit distributions of the proposed tests are also established. To derive such asymptotic, a spatial physical dependence notion is adopted to model the spatial dependent structure in this paper. The method is illustrated by simulations and data analysis.

报告人： 张荣茂，浙江大学

报告时间：2019年9月4日10:30-12:00

报告地点：精工楼7-104

报告人简介：张荣茂，浙江大学数学学院教授，数据科学中心与经济学院兼职教授，博士生导师，闽南师范大学闽江学者讲座教授。主要从事非平稳时间序列和高维空间数据的理论与应用研究，发表的杂志包括Ann. Statist.，J. Amer. Assoc. Statist.， J. Econometrics等。2015年获浙江省杰出青年基金，主持国家自然科学基金和省部级基金项目多项。现任浙江大学统计所所长，浙江省现场统计研究所副理事长，J. Korean Statist. Soc.（SCI期刊）和Intern. J. Math. Statist.编委。