Regularized estimation for the least absolute relative error models with a diverging number of covariates

DOI10.1016/j.csda.2015.10.012zbMath1468.62213OpenAlexW2190785170MaRDI QIDQ1659468

Hu Yang, Xiaochao Xia, Zhi Liu

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.10.012

zbMATH Keywords

variable selectionleast squares approximationoracle propertiesdiverging number of covariatesleast absolute relative error

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (8)

Asymptotics for least product relative error estimation and empirical likelihood with longitudinal data ⋮ Estimation and empirical likelihood for single-index multiplicative models ⋮ Optimal subsampling for multiplicative regression with massive data ⋮ A new relative error estimation for partially linear multiplicative model ⋮ Incorporating relative error criterion to conformal prediction for positive data ⋮ Penalized relative error estimation of functional multiplicative regression models with locally sparse properties ⋮ Optimal subsampling for least absolute relative error estimators with massive data ⋮ Nonconcave penalized M-estimation for the least absolute relative errors model

Uses Software

• KELLEY
• hgam

Cites Work

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• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Empirical likelihood for least absolute relative error regression
• Variable selection in the accelerated failure time model via the bridge method
• Least product relative error estimation
• Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models
• Variable selection in nonparametric additive models
• High-dimensional additive modeling
• Hedonic housing prices and the demand for clean air
• Relative-error prediction
• Nonconcave penalized likelihood with a diverging number of parameters.
• Least angle regression. (With discussion)
• Adaptive robust variable selection
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates
• Unified LASSO Estimation by Least Squares Approximation
• Predicting software errors, during development, using nonlinear regression models: a comparative study
• Prediction, Linear Regression and the Minimum Sum of Relative Errors
• Rank-based inference for the accelerated failure time model
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
• Least Absolute Relative Error Estimation
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

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