Outlier-resistant high-dimensional regression modelling based on distribution-free outlier detection and tuning parameter selection

DOI10.1080/00949655.2017.1287186OpenAlexW2589067427MaRDI QIDQ5106888

Heewon Park

Publication date: 22 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2017.1287186

zbMATH Keywords

high-dimensional datainformation criterion\(L_1\)-type regularizationdistribution-free outlier detectionrobust regression modelling

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (4)

Robust sparse regression by modeling noise as a mixture of gaussians ⋮ Outlier-resistant sequential filtering fusion for cyber-physical systems with quantized measurements under denial-of-service attacks ⋮ Truncated normal distribution-based EWMA control chart for monitoring the process mean in the presence of outliers ⋮ Outlier-resistant observer-based \(H_\infty\)-consensus control for multi-rate multi-agent systems

Uses Software

• glmnet

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Principal component selection via adaptive regularization method and generalized information criterion
• Outlier identification in high dimensions
• Estimating the dimension of a model
• Sparse least trimmed squares regression for analyzing high-dimensional large data sets
• Robust regression through the Huber's criterion and adaptive lasso penalty
• Information criteria and statistical modeling.
• Robust Coordinate Descent Algorithm Robust Solution Path for High-dimensional Sparse Regression Modeling
• Robust Linear Model Selection Based on Least Angle Regression
• Generalised information criteria in model selection
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• A Statistical View of Some Chemometrics Regression Tools
• Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria
• Robust logistic regression modelling via the elastic net-type regularization and tuning parameter selection
• Regularization and Variable Selection Via the Elastic Net
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

This page was built for publication: Outlier-resistant high-dimensional regression modelling based on distribution-free outlier detection and tuning parameter selection