Oracle Estimation of a Change Point in High-Dimensional Quantile Regression

DOI10.1080/01621459.2017.1319840zbMath1402.62033arXiv1603.00235OpenAlexW2289998504MaRDI QIDQ4559700

Sokbae Lee, Yuan Liao, Youngki Shin, Myung Hwan Seo

Publication date: 4 December 2018

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1603.00235

zbMATH Keywords

variable selectionsparsitylassoscadhigh-dimensional M-estimation

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Point estimation (62F10)

Related Items (12)

Multiscale Quantile Segmentation ⋮ A note on regression kink model ⋮ A robust bootstrap change point test for high-dimensional location parameter ⋮ Unnamed Item ⋮ Multiple change-points estimation in linear regression models via an adaptive Lasso expectile loss function ⋮ Sparsity identification in ultra-high dimensional quantile regression models with longitudinal data ⋮ Shrinkage quantile regression for panel data with multiple structural breaks ⋮ Predictive quantile regression with mixed roots and increasing dimensions: the ALQR approach ⋮ Estimation of the Spatial Weighting Matrix for Spatiotemporal Data under the Presence of Structural Breaks ⋮ Wild bootstrap inference for penalized quantile regression for longitudinal data ⋮ Sparse quantile regression ⋮ Unnamed Item

Uses Software

• CRAN
• quantreg
• wbs

Cites Work

• Unnamed Item
• Unnamed Item
• Semiparametric estimation of a binary response model with a change-point due to a covariate threshold
• The \(L_1\) penalized LAD estimator for high dimensional linear regression
• Wild binary segmentation for multiple change-point detection
• Statistics for high-dimensional data. Methods, theory and applications.
• Change-point in stochastic design regression and the bootstrap
• On the least squares estimation of multiple-regime threshold autoregressive models
• One-step sparse estimates in nonconcave penalized likelihood models
• Estimation in a Cox regression model with a change-point according to a threshold in a covariate
• On parameters of increasing dimensions
• A continuous mapping theorem for the smallest argmax functional
• Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model
• Simultaneous analysis of Lasso and Dantzig selector
• High-dimensional generalized linear models and the lasso
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Adaptive robust variable selection
• Inference under right censoring for transformation models with a change-point based on a covariate threshold
• The geometry of logconcave functions and sampling algorithms
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sample Splitting and Threshold Estimation
• Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• Group LASSO for Structural Break Time Series
• Testing for Threshold Effects in Regression Models
• Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
• Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
• Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis
• The Lasso for High Dimensional Regression with a Possible Change Point
• Multiscale Change Point Inference

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