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Activity Number: 98 - Statistical Learning for Dependent Data
Type: Invited
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #322064 View Presentation
Title: Group Orthogonal Greedy Algorithm for Change-Point Estimation of Multivariate Time Series
Author(s): Chun Yip Yau*
Companies: Chinese University of Hong Kong

We consider estimation of structural breaks in vector autoregressive models. In practice, the number of change points is usually assumed to be known and small, because a large would involve a huge amount of computational burden for parameters estimation. By reformulating the problem in a high-dimensional variable selection context, the group orthogonal greedy algorithm is proposed to estimate the structural breaks in the model. Desirable theoretical results about consistency and convergent rate of the break-location estimates are derived to lend support to the proposed methodology.

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

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