Asymptotics of \(M\)-estimation in nonlinear regression

DOI10.1007/S10114-004-0378-3zbMath1064.62078OpenAlexW2461797321MaRDI QIDQ705111

Ying Yang

Publication date: 25 January 2005

Published in: Acta Mathematica Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10114-004-0378-3

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) General nonlinear regression (62J02)

Related Items (1)

Bahadur representations of M-estimators and their applications in general linear models

Cites Work

• Unnamed Item
• The bounded law of the iterated logarithm for the weighted empirical distribution process in the non-i.i.d. case
• Asymptotic normality of minimum \(L_ 1\)-norm estimates in linear models
• Asymptotic theory of nonlinear least squares estimation
• Linear representation of \(M\)-estimates of multiple regression coefficients
• A general Bahadur representation of \(M\)-estimators and its application to linear regression with nonstochastic designs
• General \(M\)-estimation
• Asymptotic normality of \(L_ 1\)-estimators in nonlinear regression
• A Note on Quantiles in Large Samples
• Asymptotic Properties of Non-Linear Least Squares Estimators

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