國立政治大學統計學系
學 術 演 講
學 術 演 講
主講人：Prof. Michal Pesta
Department of Probability and Mathematical Statistics
Charles University, Prague, Czechia
題 目：Tensor Changepoint Detection and Eigenbootstrap
時 間：民國115年6月23日 (星期二) 下午1：30
地 點：國立政治大學商學院260903室
摘 要：
Department of Probability and Mathematical Statistics
Charles University, Prague, Czechia
題 目：Tensor Changepoint Detection and Eigenbootstrap
時 間：民國115年6月23日 (星期二) 下午1：30
地 點：國立政治大學商學院260903室
摘 要：
Tensor data consisting of multivariate outcomes over the items and across the subjects with longitudinal and cross-sectional dependence are considered. A completely distribution-free and tweaking-parameter-free detection procedure for changepoints at different locations is designed, which does not require training data. A CUSUM type test statistic is employed, and its asymptotic properties are derived for a large number of available individual profiles. The considered test is shown to be consistent. We propose an eigenbootstrap superstructure that overcomes the computational curse of dimensionality without any loss of information, while it preserves all the dependencies within and between the panels. The validity of this new and fast resampling algorithm is proved in this general setting. The empirical properties of the detection technique are investigated through a simulation study. The fully data-driven test is applied to real-world data from EEG and psychometrics.
