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Alternating Pruned Dynamic Programming for Multiple Epidemic Change-Point Estimation

In this article, we study the problem of multiple change-point detection for a univariate sequence under the epidemic setting, where the behavior of the sequence alternates between a common normal state and different epidemic states. This is a nontrivial generalization of the classical (single) epidemic change-point testing problem. To explicitly incorporate the alternating structure of the problem, we propose a novel model selection based approach for simultaneous inference on both change-points and alternating states. Using the same spirit as profile likelihood, we develop a two-stage alternating pruned dynamic programming algorithm, which conducts efficient and exact optimization of the model selection criteria and has O(n2) as the worst case computational cost. As demonstrated by extensive numerical experiments, compared to classical general-purpose multiple change-point detection procedures, the proposed method improves accuracy for both change-point estimation and model parameter estimation. We further show promising applications of the proposed algorithm to multiple testing with locally clustered signals, and demonstrate its advantages over existing methods in large scale multiple testing, in DNA copy number variation detection, and in oceanographic study. Supplementary material for this article is available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.30 No.3 September 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 808 - 821

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MODEL SELECTION
MULTIPLE TESTING
CHANGE-POINT
DNA COPY NUMBER VARIATION
EPIDEMIC ALTERNATIVE

Info Detail Spesifik

https://doi.org/10.1080/10618600.2020.1868304

Pernyataan Tanggungjawab

Zifeng Zhao, Chun Yip Yau

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