Multiple change-point detection via a screening and ranking algorithm

Ning Hao · Yue Niu · Heping Zhang D

56 citations0/0 files storedStatistica Sinica

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Advanced Statistical Process Monitoring Probability and Statistical Research Statistical Methods and Inference

Abstract

be a sequence whose underlying mean is a step function with an unknown number of the steps and unknown change points. The detection of the change points, namely the positions where the mean changes, is an important problem in such fields as engineering, economics, climatology and bioscience. This problem has attracted a lot of attention in statistics, and a variety of solutions have been proposed and implemented. However, there is scant literature on the theoretical properties of those algorithms. Here, we investigate a recently developed algorithm called the Screening and Ranking Algorithm (SaRa). We characterize the theoretical properties of SaRa and show its superiority over other commonly used algorithms. In particular, we develop a false discovery rate approach to the multiple change-point problem and show a strong sure coverage property for the SaRa.

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References

1997Limit theorems in change-point analysis 1955A Test for a Change in a Parameter Occurring at an Unknown Point 1988Estimating the number of change-points via Schwarz' criterion 2011Parametric Statistical Change Point Analysis 1964Estimating the Current Mean of a Normal Distribution which is Subjected to Changes in Time 1975On Tests for Detecting Change in Mean 1969On Detecting Changes in the Mean of Normal Variates 1993Tests for Change-Points with Epidemic Alternatives

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2020Segmentation and estimation of change-point models: False positive control and confidence regions 2014Bayesian Model for Multiple Change-points Detection in Multivariate Time Series 2020Multiple change point detection and validation in autoregressive time series data 2019Rank-based multiple change-point detection 2019A shape-based cutting and clustering algorithm for multiple change-point detection 2024Robust Narrowest Significance Pursuit: Inference for Multiple Change-Points in the Median

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