Efficient sparsity adaptive changepoint estimation

We propose a computationally efficient and sparsity adaptive procedure for estimating changes in unknown subsets of a high-dimensional data sequence. Assuming the data sequence is Gaussian, we prove that the new method successfully estimates the number and locations of changepoints with a given erro...

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Bibliographic Details
Published inElectronic journal of statistics Vol. 18; no. 2
Main Authors Moen, Per August Jarval, Glad, Ingrid Kristine, Tveten, Martin
Format Journal Article
LanguageEnglish
Published 01.01.2024
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Summary:We propose a computationally efficient and sparsity adaptive procedure for estimating changes in unknown subsets of a high-dimensional data sequence. Assuming the data sequence is Gaussian, we prove that the new method successfully estimates the number and locations of changepoints with a given error rate and under minimal conditions for all sparsities of the changing subset. Our method has computational complexity linear up to logarithmic factors in both the length and number of time series, making it applicable to large data sets. Through extensive numerical studies we show that the new methodology is highly competitive in terms of both estimation accuracy and computational cost. The practical usefulness of the method is illustrated by analysing sensor data from a hydro power plant, and an efficient R implementation is available.
Bibliography:NFR/332645
ISSN:1935-7524
1935-7524
DOI:10.1214/24-EJS2294