Abstract

Identifying the number and precise locations of multiple change points

in long sequences is a critical issue in statistics and machine learning.

However, accurate change point detection can be compromised by the presence of

local trends in the sequence when using the conventional parametric piecewiseconstant model. In this paper, we introduce an adaptive Neyman test to assess

the presence of local trends. Subsequently, we develop a novel change point detection procedure based on a partially linear model that incorporates these local

trends. Furthermore, we extend the proposed testing and estimation methods to

multidimensional cases, facilitating the identification of common change points in

array-based data. Our methods are straightforward to implement, and we evaluate their numerical performance through simulations and the analysis of SNP

genotyping data.

Information

Preprint No.SS-2024-0355
Manuscript IDSS-2024-0355
Complete AuthorsShengji Jia, Chunming Zhang, Yiming Tang
Corresponding AuthorsYiming Tang
Emailsjstangyiming@163.com

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Acknowledgments

The authors thank the Associate Editor and three reviewers for their careful review and helpful suggestions. Jia’s research was partially supported

by National Natural Science Foundation of China, Grant 12501374, and

Shanghai Natural Science Foundation, Grant 25ZR1402404. The research

of Zhang was supported by U.S. National Science Foundation grants DMS-

2013486 and DMS-1712418, and by the University of Wisconsin-Madison

Office of the Vice Chancellor for Research and Graduate Education with

Supplementary Materials

The online Supplementary Material includes the conditions and proofs of

the theoretical results, additional simulations, and an additional real data

analysis.

Supplementary materials are available for download.