Adaptive LASSO model selection in a multiphase quantile regression

DOI10.1080/02331888.2016.1151427zbMath1353.62075arXiv1309.1262OpenAlexW3101290291MaRDI QIDQ2953450

Gabriela Ciuperca

Publication date: 4 January 2017

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1309.1262

zbMATH Keywords

variable selectionchange-pointoracle propertiesselection criterionadaptive Lasso quantile

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (11)

Variable selection in high-dimensional linear model with possibly asymmetric errors ⋮ Automatic variable selection in a linear model on massive data ⋮ Adaptive group Lasso selection in quantile models ⋮ Multiple change-points estimation in linear regression models via an adaptive Lasso expectile loss function ⋮ Real-time detection of a change-point in a linear expectile model ⋮ Test by adaptive Lasso quantile method for real-time detection of a change-point ⋮ Solving multistage stochastic linear programming via regularized linear decision rules: an application to hydrothermal dispatch planning ⋮ Quantile regression for large-scale data via sparse exponential transform method ⋮ Real time change-point detection in a nonlinear quantile model ⋮ Changepoint detection by the quantile Lasso method ⋮ Adaptive fused LASSO in grouped quantile regression

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• Testing for structural change in regression quantiles
• The \(L_1\) penalized LAD estimator for high dimensional linear regression
• Schwarz information criterion based tests for a change-point in regression models
• Penalized least absolute deviations estimation for nonlinear model with change-points
• Estimating structural changes in regression quantiles
• Model selection by LASSO methods in a change-point model
• Simultaneous estimation and variable selection in median regression using Lasso-type penalty
• Composite quantile regression and the oracle model selection theory
• Simultaneous change point analysis and variable selection in a regression problem
• Estimating the number of change-points via Schwarz' criterion
• Strong representations for LAD estimators in linear models
• Estimation of multiple-regime regressions with least absolutes deviation
• Variable selection in high-dimensional partially linear additive models for composite quantile regression
• Adaptive penalized quantile regression for high dimensional data
• Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Empirical Likelihood Ratio Test for a Change-Point in Linear Regression Model
• Estimating and Testing Linear Models with Multiple Structural Changes
• Estimating and Testing Structural Changes in Multivariate Regressions

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