Rates of convergence in the asymptotic normality for some local maximum estimators

DOI10.1007/BF02473976zbMath0924.62027MaRDI QIDQ1366379

Vygantas Paulauskas

Publication date: 29 October 1997

Published in: Lithuanian Mathematical Journal (Search for Journal in Brave)

zbMATH Keywords

central limit theoremempirical process\(M\)-estimatesmooth stochastic differentiability

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Asymptotic distribution theory in statistics (62E20) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (1)

Berry-Esseen bounds for multivariate nonlinear statistics with applications to M-estimators and stochastic gradient descent algorithms

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Gaussian characterization of uniform Donsker classes of functions
• Asymptotic expansions for general statistical models. With the assist. of W. Wefelmeyer
• Asymptotic methods in statistical decision theory
• Dependence of the Berry-Esseen estimate on the dimension
• Fréchet differentiability, \(p\)-variation and uniform Donsker classes
• Asymptotic expansions in the integral and local limit theorems in Banach spaces with applications to \(\omega\)-statistics
• Distributional convergence of M-estimators under unusual rates
• Asymptotics via empirical processes. With comments and a rejoinder by the author
• The central limit theorem for empirical and quantile processes in some Banach spaces
• The Berry-Esseen bound for minimum contrast estimates
• Simulation and the Asymptotics of Optimization Estimators
• Robust Estimation of a Location Parameter
• The accuracy of the normal approximation for estimates of vector parameters
• Convergence of stochastic processes

This page was built for publication: Rates of convergence in the asymptotic normality for some local maximum estimators