High-dimensional change point detection using MOSUM-based sparse projection
- Authors
- Kim, M[Kim, Moonjung]; Baek, C[Baek, Changryong]
- Issue Date
- Feb-2022
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- change point; moving sum (MOSUM); sparse projection; high dimensional time series
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.35, no.1, pp.63 - 75
- Indexed
- KCI
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 35
- Number
- 1
- Start Page
- 63
- End Page
- 75
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/97520
- DOI
- 10.5351/KJAS.2022.35.1.063
- ISSN
- 1225-066X
- Abstract
- This paper proposes the so-called MOSUM-based sparse projection method for change points detection in high-dimensional time series. Our method is inspired by Wang and Samworth (2018), however, our method improves their method in two ways. One is to find change points all at once, so it minimizes sequential error. The other is localized so that more robust to the mean changes offsetting each other. We also propose data-driven threshold selection using block wild bootstrap. A comprehensive simulation study shows that our method performs reasonably well in finite samples. We also illustrate our method to stock prices consisting of S&P 500 index, and found four change points in recent 6 years.
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Collections - Economics > Department of Statistics > 1. Journal Articles
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