Bounds for the normal approximation of the maximum likelihood estimator

DOI10.3150/15-BEJ741zbMath1362.60017arXiv1411.2391MaRDI QIDQ502863

Andreas Anastasiou, Gesine D. Reinert

Publication date: 11 January 2017

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1411.2391

zbMATH Keywords

Stein's methodmaximum likelihood estimatornormal approximation

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Central limit and other weak theorems (60F05) Point estimation (62F10) Statistical aspects of information-theoretic topics (62B10)

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Optimal-order bounds on the rate of convergence to normality in the multivariate delta method ⋮ Assessing the multivariate normal approximation of the maximum likelihood estimator from high-dimensional, heterogeneous data ⋮ Stein’s method and the distribution of the product of zero mean correlated normal random variables ⋮ Multivariate normal approximation of the maximum likelihood estimator via the delta method ⋮ Bounds for the normal approximation of the maximum likelihood estimator from \(m\)-dependent random variables ⋮ Bounds for the asymptotic distribution of the likelihood ratio ⋮ Stein's method meets computational statistics: a review of some recent developments ⋮ Bounds in \(L^1\) Wasserstein distance on the normal approximation of general M-estimators ⋮ Approximate least squares estimation for spatial autoregressive models with covariates ⋮ Bootstrapping and sample splitting for high-dimensional, assumption-lean inference ⋮ Fixed point characterizations of continuous univariate probability distributions and their applications ⋮ Wasserstein distance error bounds for the multivariate normal approximation of the maximum likelihood estimator ⋮ Bounds for the asymptotic normality of the maximum likelihood estimator using the Delta method

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