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Activity Number: 477 - Complex Time Series Analysis
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #300528
Title: High-Dimensional Change-Point Estimation with Heterogeneous Noise
Author(s): Yining Chen*
Companies: London School of Economics
Keywords: High-dimension; Change-point; Segmentation

We study the problem of multiple change-point estimation in the mean in the high-dimensional setting, where the vector of mean changes could be either sparse or non-sparse, and where the additive noise could be heterogeneous and potentially dependent.

A three?stage procedure is proposed. First, we test whether there exist any change-points in a given interval via wild bootstrap on the lagged sample auto-covariance matrix. Second, we obtain a projection direction for that interval if change-points are suspected in that interval. Finally, we apply an existing univariate change-point estimation algorithm, such as narrowest-over-threshold method or wild binary segmentation to the projected series.

We provide theoretical results of our procedure on both the number of estimated change-points and the convergence rates of their locations. We also demonstrate its competitive empirical performance in our numerical experiments for a wide range of data generating mechanisms.

Authors who are presenting talks have a * after their name.

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