Regularization techniques in joinpoint regression

DOI10.1007/s00362-016-0823-2zbMath1351.62140OpenAlexW2519252202MaRDI QIDQ2374428

Matúš Maciak, Ivan Mizera

Publication date: 15 December 2016

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-016-0823-2

zbMATH Keywords

changepointsregularizationmodel selectionLASSOsegmented regressionpiecewise linearjoinpoint regression

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to social sciences (62P25) Parametric inference (62F99)

Related Items (3)

Testing Shape Constraints in Lasso Regularized Joinpoint Regression ⋮ Implied Volatility Surface Estimation via Quantile Regularization ⋮ Changepoint detection by the quantile Lasso method

Uses Software

• R
• covTest
• Joinpoint

Cites Work

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• A Bayesian joinpoint regression model with an unknown number of break-points
• Estimating joinpoints in continuous time scale for multiple change-point models
• The log likelihood ratio in segmented regression
• Locally adaptive regression splines
• Least angle regression. (With discussion)
• A significance test for the lasso
• Some Hypotheses Concerning Two Phase Regression Lines
• Comparability of Segmented Line Regression Models
• Quantile smoothing splines
• Penalized Triograms: Total Variation Regularization for Bivariate Smoothing
• Hierarchical Bayesian Analysis of Changepoint Problems
• Multiple Change-Point Estimation With a Total Variation Penalty
• A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data
• Fitting Segmented Curves Whose Join Points Have to be Estimated
• Inference in Two-Phase Regression

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