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Change-point Detection for Sparse and Dense Functional Data in General Dimensions
Carlos Misael Madrid Padilla · Daren Wang · Zifeng Zhao · Yi Yu

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #714
We study the problem of change-point detection and localisation for functional data sequentially observed on a general $d$-dimensional space, where we allow the functional curves to be either sparsely or densely sampled. Data of this form naturally arise in a wide range of applications such as biology, neuroscience, climatology and finance. To achieve such a task, we propose a kernel-based algorithm named functional seeded binary segmentation (FSBS). FSBS is computationally efficient, can handle discretely observed functional data, and is theoretically sound for heavy-tailed and temporally-dependent observations. Moreover, FSBS works for a general $d$-dimensional domain, which is the first in the literature of change-point estimation for functional data. We show the consistency of FSBS for multiple change-point estimation and further provide a sharp localisation error rate, which reveals an interesting phase transition phenomenon depending on the number of functional curves observed and the sampling frequency for each curve. Extensive numerical experiments illustrate the effectiveness of FSBS and its advantage over existing methods in the literature under various settings. A real data application is further conducted, where FSBS localises change-points of sea surface temperature patterns in the south Pacific attributed to El Ni\~{n}o.

Author Information

Carlos Misael Madrid Padilla (University of Notre Dame)
Daren Wang (University of Notre Dame)
Zifeng Zhao (Mendoza College of Business, University of Notre Dame)
Yi Yu (The university of Warwick)

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