Confidence surfaces for the mean of locally stationary functional time series
时间：2022-06-24
阅读量：526次
- 演讲人： 吴未迟（清华大学统计学研究中心，副教授）
- 时间：2022年06月27日 周一上午11:00
- 地点：腾讯会议 961-945-985
- 主办单位：数据科学研究中心
摘要：The problem of constructing a simultaneous confidence surface for the 2-dimensional mean function of a non-stationary functional time series is challenging as these bands can not be built on classical limit theory for the maximum absolute deviation between an estimate and the time dependent regression function. In this paper we propose new bootstrap methodology to construct such a region. Our approach is based on a Gaussian approximation for the maximum norm of sparse high-dimensional vectors approximating the maximum absolute deviation. The elimination of the zero entries produces (besides the time dependence) additionally dependencies such that ”classical” multiplier bootstrap is not applicable. To solve this issue we develop a novel multiplier bootstrap, where blocks of the coordinates of the vectors are multiplied with random variables, which mimic the specific structure between the vectors appearing in the Gaussian approximation. We prove the validity of our approach by asymptotic theory, demonstrate good finite sample properties by means of a simulation study and illustrate its applicability analyzing a data example.
报告人简介：吴未迟，清华大学工业工程系统计中心副教授，博士毕业于多伦多大学。主要研究方向为非参数统计，非平稳时间序列和泛函时间序列。在AOS， JBES， Bernoulli等杂志发表论文多篇，主持自然科学基金青年项目一项。
联系人：高照省（zhaoxing_gao@zju.edu.cn）
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