One‐step M‐estimators in the linear model, with dependent errors

DOI10.2307/3315585zbMath0801.62034OpenAlexW2004421501MaRDI QIDQ4311480

C. A. Field, Douglas P. Wiens

Publication date: 30 November 1994

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3315585

zbMATH Keywords

simulationasymptotic normalitymean squared errorbiasdependencetransformed modelregression estimatesweakly stationaryL1-estimatorcoverage probabilities of confidence intervalsrobust \(M\)-estimationautocorrelation parametersHuber-type \(M\)-estimatorsone-step \(M\)-estimatestime series of finite length

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (8)

Noise benefits to robust M-estimation of location in dependent observations ⋮ Asymptotic properties of one-step M-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 ⋮ Application of one‐step method to parameter estimation in ODE models ⋮ Confidence intervals based on robust regression ⋮ The asymptotics for studentized K-Step M-Estimators of location ⋮ Applied regression analysis bibliography update 1994-97

Uses Software

• nlmdl

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Robustness against unexpected dependence in the location model
• Nonparametric regression estimation under mixing conditions
• Small sample asymptotic expansions for multivariate M-estimates
• Minimax variance M-estimators in \(\epsilon\)-contamination models
• Minimax variance M-estimators of location in Kolmogorov neighbourhoods
• Influence functionals for time series (with discussion)
• Robust spectral regression
• Consistent regression estimation with fixed design points under dependence conditions
• Robust estimation in dependent situations
• Behavior of robust estimators in the regression model with dependent errors
• Further remarks on robust estimation in dependent situations
• A maximum variance model for robust detection and estimation with dependent data
• Robust and consistent estimates of autoregressive-moving average parameters
• One-Step Huber Estimates in the Linear Model
• Nonlinear Regression with Autocorrelated Errors
• Non-linear time series regression

This page was built for publication: One‐step M‐estimators in the linear model, with dependent errors