The accuracy of the normal approximation for estimates of vector parameters

DOI10.1007/BF00535890zbMath0252.62018MaRDI QIDQ5666000

Johann Pfanzagl

Publication date: 1973

Published in: Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete (Search for Journal in Brave)

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Central limit and other weak theorems (60F05) Point estimation (62F10)

Related Items (10)

Optimal-order bounds on the rate of convergence to normality in the multivariate delta method ⋮ Uniform and subuniform posterior robustness: The sample size problem. (With discussion) ⋮ Berry-Esseen bounds for multivariate nonlinear statistics with applications to M-estimators and stochastic gradient descent algorithms ⋮ Rates of convergence in the asymptotic normality for some local maximum estimators ⋮ Nondecomposable item response theory models: fundamental measurement in psychometrics ⋮ Weak convergence of bounded influence regression estimates with applications to repeated significance testing ⋮ ASYMPTOTIC SIZE AND A PROBLEM WITH SUBSAMPLING AND WITH THE m OUT OF n BOOTSTRAP ⋮ Speed of convergence of the distribution of the likelihood ratio statistic ⋮ Rate of convergence of maximum likelihood density estimate to a normal law ⋮ More higher-order efficiency: Concentration probability

Cites Work

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• Asymptotic expansions related to minimum contrast estimators
• The Berry-Esseen bound for minimum contrast estimates
• Further results on asymptotic normality. I
• Conditional Expectation Given A $\sigma$-Lattice and Applications
• An Asymptotic Expansion for the Maximum Likelihood Estimate of a Vector Parameter
• On the central limit theorem in R k
• The accuracy of the normal approximation for minimum contrast estimates

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