Multiple Change-Point Estimation With a Total Variation Penalty

By Zaïd Harchaoui, C. L'evy-Leduc. 2010

359 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e999877f662582b38da855a148f40c7cb230eb1a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

A robust approach for estimating change-points in the mean of an AR(1) process (2014, methodology): “In order to assess the performance of the estimation of the change-point locations, we computed the Hausdorff distance defined in the segmentation framework as follows, see [7,16]:”
Wild binary segmentation for multiple change-point detection (2014, methodology): “A diﬀerent route to reducing the computational complexity of the multiple change-point detection problem is taken by Harchaoui and L´evy-Leduc (2010) who consider the least-squares criterion with a total variation penalty, which enables them to use the LARS algorithm of Efron et al. (2004) to…”
A review of change point detection methods (2018, background): “To further lower the computational cost of change point detection with a linear penalty, an alternative formulation has been proposed in which the l 0 penalty is relaxed to a l 1 penalty [60, 129].”
Selective review of offline change point detection methods (2020, methodology): “Hausdorff The Hausdorff metric measures the robustness of detection methods [47, 48].”
Finite sample change point inference and identification for high‐dimensional mean vectors (2017, methodology): “Change point testing and estimation have a long history [45, 46, 41, 16, 31, 49, 10, 20, 33, 6, 23, 13, 37, 29, 27, 22, 24, 40, 3, 4, 7, 51, 30] and there are two major lines of change point tests in literature: sequential tests and fixed sample size tests [34].”
Inference for single and multiple change‐points in time series (2013, methodology): “For example, when the maximum number of possible change-points is known, then Harchaoui and Lévy-Leduc (2010) have shown that the convergence rate of their algorithm is O(n log n).”
A MOSUM procedure for the estimation of multiple random change points (2018, methodology): “It also coincides with the optimal rate obtained in [44] (Theorem 1) in the classical mean change model with a fixed and known number of changes and independent errors and is better than the rate of convergence obtained in [19].”
TVSum: Summarizing web videos using titles (2015, background): “Formulation: In this work, we find change-points by solving a convex problem with total variation [22, 4]:”
SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES (2015, methodology): “It builds upon some techniques that have been recently developed by Harchaoui and Lévy-Leduc (2010).”
Group LASSO for Structural Break Time Series (2014, methodology): “Also, Theorem 2.3 extends Harchaoui and Lévy-Leduc (2010) in the sense that the claim P (|An| ≥ m0) is established, not assumed.”
Optimal detection of changepoints with a linear computational cost (2011, methodology): “There are other fast algorithms for segmenting data that improve upon BS (Gey and Lebarbier 2008; Harchaoui and Levy-Leduc 2010), although these do not have the guarantee of exactness that PELT does.”
Heterogeneous change point inference (2015, background): “…F. Pein, H. Sieling and A. Munk Braun et al. (2000), Birgé and Massart (2001), Kolaczyk and Nowak (2005), Boysen et al. (2009), Harchaoui and Lévy-Leduc (2010), Jeng et al. (2010), Killick et al. (2012), Rigollet and Tsybakov (2012), Zhang and Siegmund (2012) and Fryzlewicz…”