On Existence of Explicit Asymptotically Normal Estimators in Nonlinear Regression Problems

DOI10.1007/978-3-319-51313-3_8zbMath1366.62128OpenAlexW2580928556MaRDI QIDQ5283085

Aleksandr Ivanovich Sakhanenko

Publication date: 18 July 2017

Published in: Analytical Methods in Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-51313-3_8

zbMATH Keywords

nonlinear regressionasymptotically normal estimatorsexplicit estimatorsimprovement of estimators

Mathematics Subject Classification ID

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

Related Items (3)

Existence of explicit asymptotically normal estimators in a multiple logarithmic regression problem ⋮ Toward the notion of intrinsically linear models in nonlinear regression ⋮ Constructing Explicit Estimators in Nonlinear Regression Problems

Cites Work

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• Explicit asymptotically normal estimators of an unknown parameter in a partially linear regression problem
• Explicit estimators of an unknown parameter in a power regression problem
• Rate of convergence of one- and two-step M-estimators with applications to maximum likelihood and Pitman estimators
• Quasi-likelihood and its application. A general approach to optimal parameter estimation
• Asymptotically normal estimation of a parameter in a linear-fractional regression problem
• The existence of explicit asymptotically normal estimators of an unknown parameter in a logarithmic regression problem
• Rate of convergence of \(k\)-step Newton estimators to efficient likelihood estimators

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