Change-detection-assisted multiple testing for spatiotemporal data

Yunlong Wang; Lilun Du

doi:10.1016/j.jspi.2023.03.003

Change-detection-assisted multiple testing for spatiotemporal data

Yunlong Wang
, Lilun Du^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

3 Citations (Scopus)

19 Downloads (CityUHK Scholars)

Abstract

This paper considers a large-scale multiple testing problem for spatiotemporal data with multiple change points. A data-driven procedure that aims to fully utilize the clustering information is proposed. Specifically, we first develop a new change-point detection algorithm that integrates the kernel-based aggregation of spatial observations with a global loss function at the temporal level to group data into several sets, and then derive an FDR control scheme for set-wise multiple testing. Under some mild conditions on the spatiotemporal dependence structure, FDR is shown to be strongly controlled. Theoretical analysis and numerical studies demonstrate the advantages of the algorithm over competing methods.

© 2023 Published by Elsevier B.V.

Original language	English
Pages (from-to)	57-74
Journal	Journal of Statistical Planning and Inference
Volume	227
Online published	24 Mar 2023
DOIs	https://doi.org/10.1016/j.jspi.2023.03.003
Publication status	Published - Dec 2023

Bibliographical note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Funding

The authors thank the editor, the associate editor, and one anonymous referee for many helpful comments that have resulted in significant improvements in the article. Dr. Du’s research was partially supported by Hong Kong RGC ECS 26301216 and Hong Kong RGC GRF 16302620.

Research Keywords

Spatiotemporal data
False discovery rate
Multiple change-point detection

Publisher's Copyright Statement

COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/.

RGC Funding Information

RGC-funded

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10.1016/j.jspi.2023.03.003

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title = "Change-detection-assisted multiple testing for spatiotemporal data",

abstract = "This paper considers a large-scale multiple testing problem for spatiotemporal data with multiple change points. A data-driven procedure that aims to fully utilize the clustering information is proposed. Specifically, we first develop a new change-point detection algorithm that integrates the kernel-based aggregation of spatial observations with a global loss function at the temporal level to group data into several sets, and then derive an FDR control scheme for set-wise multiple testing. Under some mild conditions on the spatiotemporal dependence structure, FDR is shown to be strongly controlled. Theoretical analysis and numerical studies demonstrate the advantages of the algorithm over competing methods. {\textcopyright} 2023 Published by Elsevier B.V.",

keywords = "Spatiotemporal data, False discovery rate, Multiple change-point detection",

author = "Yunlong Wang and Lilun Du",

note = "Research Unit(s) information for this publication is provided by the author(s) concerned.",

year = "2023",

month = dec,

doi = "10.1016/j.jspi.2023.03.003",

language = "English",

volume = "227",

pages = "57--74",

journal = "Journal of Statistical Planning and Inference",

issn = "0378-3758",

publisher = "Elsevier B.V.",

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TY - JOUR

T1 - Change-detection-assisted multiple testing for spatiotemporal data

AU - Wang, Yunlong

AU - Du, Lilun

N1 - Research Unit(s) information for this publication is provided by the author(s) concerned.

PY - 2023/12

Y1 - 2023/12

N2 - This paper considers a large-scale multiple testing problem for spatiotemporal data with multiple change points. A data-driven procedure that aims to fully utilize the clustering information is proposed. Specifically, we first develop a new change-point detection algorithm that integrates the kernel-based aggregation of spatial observations with a global loss function at the temporal level to group data into several sets, and then derive an FDR control scheme for set-wise multiple testing. Under some mild conditions on the spatiotemporal dependence structure, FDR is shown to be strongly controlled. Theoretical analysis and numerical studies demonstrate the advantages of the algorithm over competing methods. © 2023 Published by Elsevier B.V.

AB - This paper considers a large-scale multiple testing problem for spatiotemporal data with multiple change points. A data-driven procedure that aims to fully utilize the clustering information is proposed. Specifically, we first develop a new change-point detection algorithm that integrates the kernel-based aggregation of spatial observations with a global loss function at the temporal level to group data into several sets, and then derive an FDR control scheme for set-wise multiple testing. Under some mild conditions on the spatiotemporal dependence structure, FDR is shown to be strongly controlled. Theoretical analysis and numerical studies demonstrate the advantages of the algorithm over competing methods. © 2023 Published by Elsevier B.V.

KW - Spatiotemporal data

KW - False discovery rate

KW - Multiple change-point detection

UR - https://www.scopus.com/pages/publications/85153101201

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85153101201&origin=recordpage

U2 - 10.1016/j.jspi.2023.03.003

DO - 10.1016/j.jspi.2023.03.003

M3 - RGC 21 - Publication in refereed journal

SN - 0378-3758

VL - 227

SP - 57

EP - 74

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

ER -

Change-detection-assisted multiple testing for spatiotemporal data

Abstract

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Research Keywords

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GRF: A Unified Framework for Multiple Testing with Auxiliary Information: a Sample-splitting Approach

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Change-detection-assisted multiple testing for spatiotemporal data

Abstract

Bibliographical note

Funding

Research Keywords

Publisher's Copyright Statement

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

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GRF: A Unified Framework for Multiple Testing with Auxiliary Information: a Sample-splitting Approach

Cite this