Least sum of squares of trimmed residuals regression
From MaRDI portal
Publication:6184883
DOI10.1214/23-ejs2164arXiv2202.10329OpenAlexW4387465005MaRDI QIDQ6184883
Publication date: 5 January 2024
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2202.10329
consistencyrobust regressionfinite sample breakdown pointtrimmed residualsapproximate computation algorithm
Linear regression; mixed models (62J05) Linear inference, regression (62J99) Nonparametric inference (62G99)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- High breakdown-point and high efficiency robust estimates for regression
- Computation of projection regression depth and its induced median
- Multidimensional trimming based on projection depth
- Cube root asymptotics
- Trimmed and Winsorized means based on a scaled deviation
- Breakdown properties of location estimates based on halfspace depth and projected outlyingness
- The anonymous professor Gergonne
- Hedonic housing prices and the demand for clean air
- Improved feasible solution algorithms for high breakdown estimation.
- The influence functions for the least trimmed squares and the least trimmed absolute deviations estimators
- Maximal inequalities for degenerate \(U\)-processes with applications to optimization estimators
- The feasible solution algorithm for least trimmed squares regression
- Projection-based depth functions and associated medians
- The trimmed mean in the linear model
- Robust regression: Asymptotics, conjectures and Monte Carlo
- General notions of statistical depth function.
- Weak convergence and empirical processes. With applications to statistics
- On general notions of depth for regression
- Robustness of the deepest projection regression functional
- Large sample properties of the regression depth induced median
- Approximation Theorems of Mathematical Statistics
- Least Median of Squares Regression
- The influence function of penalized regression estimators
- High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale
- Trimmed Least Squares Estimation in the Linear Model
- One-Step Huber Estimates in the Linear Model
- Asymptotic Statistics
- Regression Depth
- The Least Trimmed Differences Regression Estimator and Alternatives
- A new approach for the computation of halfspace depth in high dimensions
- The Limiting Distribution of the Maximum Rank Correlation Estimator
- Robust Statistics
- Robust Estimation of a Location Parameter
- Convergence of stochastic processes
This page was built for publication: Least sum of squares of trimmed residuals regression