A journey in single steps: robust one-step \(M\)-estimation in linear regression
From MaRDI portal
Publication:1600727
DOI10.1016/S0378-3758(01)00228-2zbMath0988.62040MaRDI QIDQ1600727
A. H. Welsh, Elvezio Ronchetti
Publication date: 16 June 2002
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
outliersinfluence functionbreakdown pointrejection methodM-estimatorS-estimatoriteratively reweighted least squares estimatormethod of scoring estimatorNewton-Raphson estimator
Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35) Diagnostics, and linear inference and regression (62J20)
Related Items
Analysis of the forward search using some new results for martingales and empirical processes, Asymptotic Analysis of Iterated 1-Step Huber-Skip M-Estimators with Varying Cut-Offs, Constructing initial estimators in one-step estimation procedures of nonlinear regression, Robust estimation and variable selection in sufficient dimension reduction, Asymptotic properties of one-step M-estimators, Reweighted least trimmed squares: an alternative to one-step estimators, Asymptotic Properties of One-Step Weighted $M$-Estimators with Applications to Regression, Asymptotic normality of one-step \(M\)-estimators based on non-identically distributed observations, A robust version of the hurdle model, Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change, Semiparametrically weighted robust estimation of regression models, One-stepM-estimators: Jones and Faddy's skewedt-distribution, Semiparametric robust estimation of truncated and censored regression models, Least trimmed squares in nonlinear regression under dependence, Robust variable selection through MAVE, DELETE-2 AND DELETE-3 JACKKNIFE PROCEDURES FOR UNMASKING IN REGRESSION, Robust estimation of dimension reduction space, Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models, Discussion of the Paper “Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models” by Johansen & Nielsen, Robust fitting of hidden Markov regression models under a longitudinal setting, Valid Inference Corrected for Outlier Removal, One-step estimation of spatial dependence parameters: Properties and extensions of the APLE statistic, Scale calibration for high-dimensional robust regression, Heteroscedasticity testing after outlier removal, Discussion: The forward search: theory and data analysis
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Effect of the initial estimator on the asymptotic behavior of one-step M- estimator
- Asymptotic relations between L- and M-estimators in the linear model
- Cube root asymptotics
- One-step L-estimators for the linear model
- A local breakdown property of robust tests in linear regression
- Directions in robust statistics and diagnostics. Part I. Proceedings of the IMA 1989 summer program 'Robustness, diagnostics, computing and graphics in statistics', Minneapolis, MN (USA)
- Reweighted LS estimators converge at the same rate as the initial estimator
- Asymptotics of reweighted estimators of multivariate location and scatter
- The trimmed mean in the linear model
- The asymptotics of S-estimators in the linear regression model
- Least Median of Squares Regression
- Outlier..........s
- The Breakdown Points of the Mean Combined with Some Rejection Rules
- Asymptotics for one-step m-estimators in regression with application to combining efficiency and high breakdown point
- On the Strong Law of Large Numbers and Related Results for Quasi-Stationary Sequences
- Trimmed Least Squares Estimation in the Linear Model
- Influence Functions of Iteratively Reweighted Least Squares Estimators
- On One-Step GM Estimates and Stability of Inferences in Linear Regression
- The Influence Curve and Its Role in Robust Estimation
- One-Step Huber Estimates in the Linear Model
- Identification of Outliers in Multivariate Data
- Regression Depth
- Simon Newcomb, Percy Daniell, and the History of Robust Estimation 1885-1920
- A Note on Quantiles in Large Samples
- On the Asymptotic Distribution of Differentiable Statistical Functions
- Robust Statistics