Refinement of Fisher's One-Step Estimators in the Case of Slowly Converging Initial Estimators

DOI10.1137/S0040585X97T987478zbMath1384.62074OpenAlexW2291543148MaRDI QIDQ2790681

Yuliana Yu. Linke

Publication date: 8 March 2016

Published in: Theory of Probability & Its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/s0040585x97t987478

zbMATH Keywords

Newton's methodmaximum likelihood estimatorinitial estimatorone-step estimatorapproximation to maximum likelihood estimator

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12)

Related Items (5)

On the multi-step MLE-process for ergodic diffusion ⋮ Asymptotic properties of one-step M-estimators ⋮ Asymptotic Properties of One-Step Weighted $M$-Estimators with Applications to Regression ⋮ Asymptotic normality of one-step \(M\)-estimators based on non-identically distributed observations ⋮ Constructing Explicit Estimators in Nonlinear Regression Problems

Cites Work

• Effect of the initial estimator on the asymptotic behavior of one-step M- estimator
• On conditions for asymptotic normality of Fisher's one-step estimators in one-parameter families of distributions
• Rate of convergence of one- and two-step M-estimators with applications to maximum likelihood and Pitman estimators
• Rate of convergence of \(k\)-step Newton estimators to efficient likelihood estimators
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