报告人：张荣茂教授 浙江大学
线上会议：#腾讯会议，401-918-148
会议时间：2022/10/28 15:00-15:45
主持人：夏强教授

报告摘要:Stationarity test is an important issue in spatial data analysis. Let Z(s), s∈Rd be a random field and Jn(ω) be its discrete Fourier transform (DFT) at frequency ω. It is known that Jn(ω) at fundamental frequencies are asymptotically uncorrelated if and only if Z(s) is second-order stationary, see Bandyopadhyay and Subba Rao (2017). A test for stationarity based on the sample covariance of Jn(ω) can be constructed. However, such a test always performs very poor because its asymptotic variance is difficult to estimate accurately in finite sample, which leads to small size and power. To address this issue, this paper proposes two self-normalized statistics based on extreme values and partial sum of the sample covariance of the DFTs, which allow the lag order of the frequencies in constructing the statistics to be fixed or divergent. Under certain regular conditions, it is shown that the proposed tests converge to functionals of Brownian motion. Simulations and two real data examples confirm good performance of the proposed extreme test.

报告人简介：张荣茂，现为浙江大学数学学院教授、浙江大学统计所所长，浙江省现场统计研究所副理事长。2004年在浙江大学获得博士学位，2004年7月至2006年6月在北京大学从事博士后研究，2006年至今在浙江大学工作，多次访问香港科大、香港中文大学和伦敦政治经济学院。主要从事非平稳金融时间序列和高维空间计量经济模型的理论与应用研究，已发表SSCI/SCI论文50多篇，发表的杂志包括Annals of Statistics，Journal of the American Statistical Association，Journal of Econometrics，Econometric Theory, Journal of Business and Economic Statistics等统计与计量经济杂志。2015年获浙江省杰出青年基金，主持浙江省重点基金项目1项、国家自然科学基金和省部级基金项目多项，2021年获第一届统计学科学技术进步奖（三等奖）、2022年获浙江省自然科学奖（二等奖），现任J. Korean Statist. Soc.等杂志的编委。

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