Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method
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Publication:5861495
DOI10.1080/02664763.2020.1722079OpenAlexW3005136712MaRDI QIDQ5861495
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Publication date: 1 March 2022
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2020.1722079
robust regressionvariable selectionLASSOoutlier detectionpenalized weighted least absolute deviation
Uses Software
Cites Work
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- The minimum regularized covariance determinant estimator
- The Adaptive Lasso and Its Oracle Properties
- Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression
- High breakdown-point and high efficiency robust estimates for regression
- Composite quantile regression and the oracle model selection theory
- One-step sparse estimates in nonconcave penalized likelihood models
- Robust weighted LAD regression
- Outlier identification in high dimensions
- BACON: blocked adaptive computationally efficient outlier nominators.
- An exponential-squared estimator in the autoregressive model with heavy-tailed errors
- Penalized weighted least absolute deviation regression
- A class of robust and fully efficient regression estimators
- Sparse least trimmed squares regression for analyzing high-dimensional large data sets
- Robust regression through the Huber's criterion and adaptive lasso penalty
- Robust estimation for the varying coefficient partially nonlinear models
- A robust and efficient estimation method for partially nonlinear models via a new MM algorithm
- Outlier detection for compositional data using robust methods
- New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
- Adaptive robust variable selection
- Least trimmed squares in nonlinear regression under dependence
- Finding an Unknown Number of Multivariate Outliers
- Genetic algorithms for outlier detection in multiple regression with different information criteria
- Outlier Detection Using Nonconvex Penalized Regression
- Procedures for the identification of multiple influential observations in linear regression
- Least Median of Squares Regression
- Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
- High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Modern outlier detection methods and thier effect on subsequent inference
- Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
- Fully efficient robust estimation, outlier detection, and variable selection via penalized regression
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Identification of multiple high leverage points in logistic regression
- Robust estimation in partially linear regression models
- Robust Variable Selection With Exponential Squared Loss
- A Cluster-Based Outlier Detection Scheme for Multivariate Data
