Consistency of \(M\)-estimates in general regression models

DOI10.1006/jmva.1994.1036zbMath0872.62071OpenAlexW2072565754MaRDI QIDQ1333193

Friedrich Liese, Igor Vajda

Publication date: 19 October 1997

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1994.1036

zbMATH Keywords

consistencyuniform convergenceM-estimatorsidentifiability

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35) General nonlinear regression (62J02)

Related Items (21)

Strong convergence of estimators in nonlinear autoregressive models ⋮ Symmetric regression quantile and its application to robust estimation for the nonlinear regression model ⋮ Conditions equivalent to consistency of approximate MLE's for stochastic processes ⋮ Robust estimation of constant and time-varying parameters in nonlinear ordinary differential equation models ⋮ A general Bahadur representation of \(M\)-estimators and its application to linear regression with nonstochastic designs ⋮ Necessary and sufficient conditions for consistency of generalized \(M\)- estimates ⋮ Sensitivity analysis of \(M\)-estimates ⋮ Robust estimation of parameters in nonlinear ordinary differential equation models ⋮ On asymptotically optimal estimates for general observations ⋮ BOUNDEDNESS OF M-ESTIMATORS FOR LINEAR REGRESSION IN TIME SERIES ⋮ Change-Point Estimation as a Nonlinear Regression Problem ⋮ M-estimators of structural parameters in pseudolinear models. ⋮ On consistent statistical procedures in regression ⋮ \(L_1\)-estimation in linear models with heterogeneous white noise ⋮ Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Necessary and sufficient conditions for consistency of \(M\)-estimates in regression models with general errors ⋮ Unnamed Item ⋮ Strong convergence of estimators as \(\varepsilon_n\)-minimisers of optimisation problems

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