Sequential Multiple Testing with Three Hypotheses and Known Number of Streams Following Each Hypothesis

Yiming Xing; Yifan Chen; Tianyi Qu

doi:10.1109/ICASSP55912.2026.11463618

Sequential Multiple Testing with Three Hypotheses and Known Number of Streams Following Each Hypothesis

Yiming Xing^*
, Yifan Chen
, Tianyi Qu

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

In this work, we consider the problem of testing the marginal distributions of multiple independent, sequentially observed data streams, where for each stream there are three hypotheses to select from. We assume that the exact number of streams following each hypothesis is a priori known. The goal is to devise a sequential multiple testing procedure that (i) controls the familywise error probabilities of all types below arbitrary user-specified levels, and (ii) achieves the minimum expected sample size under all possible distributions and among all testing procedures that control the error probabilities below the same levels, asymptotically as the levels go to zero. The extension to arbitrary number of hypotheses and arbitrary form of prior information is set as future directions. A preprint version is available at https://doi.org/10.13140/RG.2.2.14922.43209.

Original language	English
Title of host publication	ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	IEEE
Pages	86-90
Number of pages	5
ISBN (Electronic)	9798331567019
ISBN (Print)	9798331567026
DOIs	https://doi.org/10.1109/ICASSP55912.2026.11463618
Publication status	Published - 3 May 2026
Event	2026 IEEE International Conference on Acoustics, Speech and Signal Processing - Centre de Convencions Internacional de Barcelona, Barcelona, Spain Duration: 3 May 2026 → 8 May 2026 https://2026.ieeeicassp.org/ (Conference website) https://2026.ieeeicassp.org/technical-program/ (Conference program schedule) https://ieeexplore.ieee.org/xpl/conhome/11460365/proceeding (Conference proceeding)

Publication series

Name	IEEE International Conference on Acoustics, Speech and Signal Processing
Publisher	IEEE
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	2026 IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated title	ICASSP 2026
Country/Territory	Spain
City	Barcelona
Period	3/05/26 → 8/05/26
Internet address	https://2026.ieeeicassp.org/ (Conference website) https://2026.ieeeicassp.org/technical-program/ (Conference program schedule) https://ieeexplore.ieee.org/xpl/conhome/11460365/proceeding (Conference proceeding)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

User-Defined Keywords

Asymptotic optimality
likelihood ratio test
multihypothesis testing
prior information
sequential multiple testing

Access to Document

10.1109/ICASSP55912.2026.11463618

Cite this

Xing, Y., Chen, Y., & Qu, T. (2026). Sequential Multiple Testing with Three Hypotheses and Known Number of Streams Following Each Hypothesis. In ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 86-90). (IEEE International Conference on Acoustics, Speech and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP55912.2026.11463618

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title = "Sequential Multiple Testing with Three Hypotheses and Known Number of Streams Following Each Hypothesis",

abstract = "In this work, we consider the problem of testing the marginal distributions of multiple independent, sequentially observed data streams, where for each stream there are three hypotheses to select from. We assume that the exact number of streams following each hypothesis is a priori known. The goal is to devise a sequential multiple testing procedure that (i) controls the familywise error probabilities of all types below arbitrary user-specified levels, and (ii) achieves the minimum expected sample size under all possible distributions and among all testing procedures that control the error probabilities below the same levels, asymptotically as the levels go to zero. The extension to arbitrary number of hypotheses and arbitrary form of prior information is set as future directions. A preprint version is available at https://doi.org/10.13140/RG.2.2.14922.43209.",

keywords = "Asymptotic optimality, likelihood ratio test, multihypothesis testing, prior information, sequential multiple testing",

author = "Yiming Xing and Yifan Chen and Tianyi Qu",

note = "Yiming Xing is supported by National Natural Foundation of China, Young Scientists Fund, No. 12501379; Shanghai Rising Star Program, No. 24YF2748500; and Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science (East China Normal University), Ministry of Education, No. KLATASDS2501. Yifan Chen acknowledges funding from National Natural Science Foundation of China (NSFC) under grant 62502408, Research Grants Council (RGC) under grant 22303424, GuangDong Basic and Applied Basic Research Foundation under grant 2025A1515010259, and Guangdong and Hong Kong Universities {"}1+1+1{"} Joint Research Collaboration Scheme under grant 2025A050500. Publisher Copyright: {\textcopyright}2026 IEEE; 2026 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2026 ; Conference date: 03-05-2026 Through 08-05-2026",

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Xing, Y, Chen, Y & Qu, T 2026, Sequential Multiple Testing with Three Hypotheses and Known Number of Streams Following Each Hypothesis. in ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, pp. 86-90, 2026 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, 3/05/26. https://doi.org/10.1109/ICASSP55912.2026.11463618

Sequential Multiple Testing with Three Hypotheses and Known Number of Streams Following Each Hypothesis. / Xing, Yiming; Chen, Yifan; Qu, Tianyi.
ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2026. p. 86-90 (IEEE International Conference on Acoustics, Speech and Signal Processing).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Sequential Multiple Testing with Three Hypotheses and Known Number of Streams Following Each Hypothesis

AU - Xing, Yiming

AU - Chen, Yifan

AU - Qu, Tianyi

N1 - Yiming Xing is supported by National Natural Foundation of China, Young Scientists Fund, No. 12501379; Shanghai Rising Star Program, No. 24YF2748500; and Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science (East China Normal University), Ministry of Education, No. KLATASDS2501. Yifan Chen acknowledges funding from National Natural Science Foundation of China (NSFC) under grant 62502408, Research Grants Council (RGC) under grant 22303424, GuangDong Basic and Applied Basic Research Foundation under grant 2025A1515010259, and Guangdong and Hong Kong Universities "1+1+1" Joint Research Collaboration Scheme under grant 2025A050500. Publisher Copyright: ©2026 IEEE

PY - 2026/5/3

Y1 - 2026/5/3

N2 - In this work, we consider the problem of testing the marginal distributions of multiple independent, sequentially observed data streams, where for each stream there are three hypotheses to select from. We assume that the exact number of streams following each hypothesis is a priori known. The goal is to devise a sequential multiple testing procedure that (i) controls the familywise error probabilities of all types below arbitrary user-specified levels, and (ii) achieves the minimum expected sample size under all possible distributions and among all testing procedures that control the error probabilities below the same levels, asymptotically as the levels go to zero. The extension to arbitrary number of hypotheses and arbitrary form of prior information is set as future directions. A preprint version is available at https://doi.org/10.13140/RG.2.2.14922.43209.

AB - In this work, we consider the problem of testing the marginal distributions of multiple independent, sequentially observed data streams, where for each stream there are three hypotheses to select from. We assume that the exact number of streams following each hypothesis is a priori known. The goal is to devise a sequential multiple testing procedure that (i) controls the familywise error probabilities of all types below arbitrary user-specified levels, and (ii) achieves the minimum expected sample size under all possible distributions and among all testing procedures that control the error probabilities below the same levels, asymptotically as the levels go to zero. The extension to arbitrary number of hypotheses and arbitrary form of prior information is set as future directions. A preprint version is available at https://doi.org/10.13140/RG.2.2.14922.43209.

KW - Asymptotic optimality

KW - likelihood ratio test

KW - multihypothesis testing

KW - prior information

KW - sequential multiple testing

UR - https://ieeexplore.ieee.org/document/11463618/

U2 - 10.1109/ICASSP55912.2026.11463618

DO - 10.1109/ICASSP55912.2026.11463618

M3 - Conference proceeding

SN - 9798331567026

T3 - IEEE International Conference on Acoustics, Speech and Signal Processing

SP - 86

EP - 90

BT - ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

T2 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing

Y2 - 3 May 2026 through 8 May 2026

ER -

Sequential Multiple Testing with Three Hypotheses and Known Number of Streams Following Each Hypothesis

Abstract

Publication series

Conference

UN SDGs

User-Defined Keywords

Access to Document

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