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題名 Greedy Segmentation for a Functional Data Sequence
作者 黃子銘
Huang, Tzee-Ming;Chen, Yu-Ting;Chiou, Jeng-Min
貢獻者 統計系
關鍵詞 Covariance operator; Functional principal components; Multiple changepoints; Projection; Significance test
日期 2023-06
上傳時間 13-Dec-2023 13:55:04 (UTC+8)
摘要 We present a new approach known as greedy segmentation (GS) to identify multiple changepoints for a functional data sequence. The proposed multiple changepoint detection criterion links detectability with the projection onto a suitably chosen subspace and the changepoint locations. The changepoint estimator identifies the true changepoints for any predetermined number of changepoint candidates, either over-reporting or under-reporting. This theoretical finding supports the proposed GS estimator, which can be efficiently obtained in a greedy manner. The GS estimator’s consistency holds without being restricted to the conventional at most one changepoint condition, and it is robust to the relative positions of the changepoints. Based on the GS estimator, the test statistic’s asymptotic distribution leads to the novel GS algorithm, which identifies the number and locations of changepoints. Using intensive simulation studies, we compare the finite sample performance of the GS approach with other competing methods. We also apply our method to temporal changepoint detection in weather datasets.
關聯 Journal of the American Statistical Association , Vol.118, No.542, pp.959-971
資料類型 article
DOI https://doi.org/10.1080/01621459.2021.1963261
dc.contributor 統計系
dc.creator (作者) 黃子銘
dc.creator (作者) Huang, Tzee-Ming;Chen, Yu-Ting;Chiou, Jeng-Min
dc.date (日期) 2023-06
dc.date.accessioned 13-Dec-2023 13:55:04 (UTC+8)-
dc.date.available 13-Dec-2023 13:55:04 (UTC+8)-
dc.date.issued (上傳時間) 13-Dec-2023 13:55:04 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/148697-
dc.description.abstract (摘要) We present a new approach known as greedy segmentation (GS) to identify multiple changepoints for a functional data sequence. The proposed multiple changepoint detection criterion links detectability with the projection onto a suitably chosen subspace and the changepoint locations. The changepoint estimator identifies the true changepoints for any predetermined number of changepoint candidates, either over-reporting or under-reporting. This theoretical finding supports the proposed GS estimator, which can be efficiently obtained in a greedy manner. The GS estimator’s consistency holds without being restricted to the conventional at most one changepoint condition, and it is robust to the relative positions of the changepoints. Based on the GS estimator, the test statistic’s asymptotic distribution leads to the novel GS algorithm, which identifies the number and locations of changepoints. Using intensive simulation studies, we compare the finite sample performance of the GS approach with other competing methods. We also apply our method to temporal changepoint detection in weather datasets.
dc.format.extent 109 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Journal of the American Statistical Association , Vol.118, No.542, pp.959-971
dc.subject (關鍵詞) Covariance operator; Functional principal components; Multiple changepoints; Projection; Significance test
dc.title (題名) Greedy Segmentation for a Functional Data Sequence
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1080/01621459.2021.1963261
dc.doi.uri (DOI) https://doi.org/10.1080/01621459.2021.1963261