General m-esttmators and applications to bounded influence estimation for non-linear regression

DOI10.1080/03610928308828628zbMath0544.62059OpenAlexW1989262972MaRDI QIDQ3333909

Ricardo Fraiman

Publication date: 1983

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928308828628

zbMATH Keywords

consistencyasymptotic normalityM-estimatornumerical iterative procedurebounded influence curve

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) General nonlinear regression (62J02) Probabilistic methods, stochastic differential equations (65C99)

Related Items (6)

Continuity and differentiability of regression M functionals ⋮ Asymptotic properties of M-estimators in linear and nonlinear multivariate regression models ⋮ Minimum distance density-based estimation ⋮ Robust m-estimators ⋮ Robust inference for nonlinear regression models ⋮ Robust tests in nonlinear regression models

Cites Work

• A note on differentials and the CLT and LIL for statistical functions, with application to M-estimates
• Robust estimation of a location parameter in the presence of asymmetry
• Asymptotic behavior of iterative M-estimartors for location
• Asymptotic behavior of general M-estimates for regression and scale with random carriers
• Estimation in Linear Regression Models with Disparate Data Points
• Robust regression using iteratively reweighted least-squares
• Nonlinear Least Squares Estimation

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