Linear Time Dynamic Programming for Computing Breakpoints in the Regularization Path of Models Selected From a Finite Set

DOI10.1080/10618600.2021.2000422OpenAlexW3214575302MaRDI QIDQ5084430

Joseph Vargovich, Toby Hocking

Publication date: 24 June 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.02808

zbMATH Keywords

dynamic programmingmodel selectionbinary segmentationchangepoint detection

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• UCI-ml
• PDCO
• sparsenet

Cites Work

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• On optimal multiple changepoint algorithms for large data
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• Nearly unbiased variable selection under minimax concave penalty
• Using penalized contrasts for the change-point problem
• Best subset selection via a modern optimization lens
• Algorithms for the optimal identification of segment neighborhoods
• Low-rank matrix completion using nuclear norm minimization and facial reduction
• SparseNet: Coordinate Descent With Nonconvex Penalties
• On the approximation of curves by line segments using dynamic programming
• Atomic Decomposition by Basis Pursuit
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• A Cluster Analysis Method for Grouping Means in the Analysis of Variance
• Matching pursuits with time-frequency dictionaries
• Optimal Detection of Changepoints With a Linear Computational Cost

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