Annals of Statistics

Segmentation and estimation of change-point models: False positive control and confidence regions

Xiao Fang, Jian Li, and David Siegmund

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To segment a sequence of independent random variables at an unknown number of change-points, we introduce new procedures that are based on thresholding the likelihood ratio statistic, and give approximations for the probability of a false positive error when there are no change-points. We also study confidence regions based on the likelihood ratio statistic for the change-points and joint confidence regions for the change-points and the parameter values. Applications to segment array CGH data are discussed.

Article information

Ann. Statist., Volume 48, Number 3 (2020), 1615-1647.

Received: March 2018
Revised: February 2019
First available in Project Euclid: 17 July 2020

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation 62G15: Tolerance and confidence regions

Array CGH analysis change-points confidence regions exponential families likelihood ratio statistics


Fang, Xiao; Li, Jian; Siegmund, David. Segmentation and estimation of change-point models: False positive control and confidence regions. Ann. Statist. 48 (2020), no. 3, 1615--1647. doi:10.1214/19-AOS1861.

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Supplemental materials

  • Supplement to “Segmentation and estimation of change-point models: False positive control and confidence regions.”. This supplement contains proofs of Theorems 2.1 and 3.1.