Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)

2020/06/23 by Kovács, Solt, Li, Housen, Bühlmann, Peter
#Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2006.12806

Abstract

In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives. Furthermore, we investigate a novel estimator of the noise level, which improves many existing model selection procedures (including the steepest drop to low levels), particularly for challenging frequent change point scenarios with low signal-to-noise ratios.

Related

• Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection
• Frequent or Systematic Changes? discussion on "Detecting possibly\n frequent change-points: Wild Binary Segmentation 2 and steepest-drop model\n selection."
• A novel changepoint detection algorithm
• A Review of Changepoint Detection Models
• Short Communication: Detecting Possibly Frequent Change-points: Wild Binary Segmentation 2