The strong consistency of M-estimators in linear models
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Publication:795448
DOI10.1016/0047-259X(84)90069-1zbMath0542.62057OpenAlexW2047847770MaRDI QIDQ795448
Publication date: 1984
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0047-259x(84)90069-1
linear modelsinformation matricesmaximum and minimum eigenvaluesscale equivariancestrong consistency of M-estimators
Related Items (8)
A theorem on uniform convergence of stochastic functions with applications ⋮ Asymptotics of M‐estimator in multivariate linear regression models for a class of random errors ⋮ Asymptotic properties of M estimators in classical linear models with φ -mixing random errors ⋮ An exponential inequality and its application to \(M\) estimators in multiple linear models ⋮ Asymptotic property of \(M\) estimator in classical linear models under dependent random errors ⋮ On the Strong Consistency of M-Estimates in Linear Models for Negatively Superadditive Dependent Errors ⋮ Asymptotics of estimating equations under natural conditions. ⋮ Weak linear representation of M-estimaton in GLMs with dependent errors
Cites Work
- Strong consistency of least squares estimates in multiple regression II
- Strong consistency of least squares estimators in linear regression models
- Convergence systems and strong consistency of least squares estimates in regression models
- Asymptotic theory of nonlinear least squares estimation
- Strong consistency of least squares estimators in regression with correlated disturbances
- Asymptotic behavior of M-estimators for the linear model
- Robust regression: Asymptotics, conjectures and Monte Carlo
- On the convergence of sums of independent Banach space valued random variables
- Asymptotic Normality and Consistency of the Least Squares Estimators for Families of Linear Regressions
- Strong consistency of least squares estimates in multiple regression
- Robust Estimation of a Location Parameter
- Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems
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