报告题目：Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation
报 告 人：Zhou Zhou教授 加拿大多伦多大学
报告时间：2020年9月4日 8:30-9:30
报告地点：腾讯会议 ID：509 946 293会议密码：200904
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校内联系人：朱复康 fzhu@jlu.edu.cn
报告摘要：
We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by non-stationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect all oscillatory frequencies and the corresponding change points within an accurate range with a prescribed probability asymptotically. This study is motivated by oscillatory frequency estimation and change point detection problems encountered in physiological time series analysis. An application to spindle detection and estimation in sleep EEG data is used to illustrate the usefulness of the proposed methodology. A Gaussian approximation scheme and an overlapping-block multiplier bootstrap methodology for sums of complex-valued high dimensional non-stationary time series without variance lower bounds are established, which could be of independent interest.
报告人简介：
Zhou Zhou obtained his Ph.D. in Statistics from the University of Chicago in 2009. He is now an Associate Professor of Statistics with tenure at the University of Toronto. Zhou’s major research interests lie in non-stationary time series analysis, non- and semi- parametric methods, time-frequency analysis, change points analysis, and functional and longitudinal data analysis.
