Segmentation and Estimation of Change-point Models: False Positive Control and Confidence Regions

Abstract

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. 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.

48 pages

References in corpus (3)

• The screening and ranking algorithm to detect DNA copy number variations
• Evaluating stationarity via change-point alternatives with applications to fMRI data
• Multi-sequence segmentation via score and higher-criticism tests

Cited by in corpus (1)

• Multiscale Change-point Segmentation: Beyond Step Functions