Bounds for the asymptotic normality of the maximum likelihood estimator using the Delta method

zbMath1362.62046arXiv1508.04948MaRDI QIDQ2974526

Andreas Anastasiou, Christophe Ley

Publication date: 10 April 2017

Full work available at URL: https://arxiv.org/abs/1508.04948

zbMATH Keywords

Stein's methodmaximum likelihood estimatornormal approximationDelta method

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Approximations to statistical distributions (nonasymptotic) (62E17)

Related Items (8)

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 ⋮ 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 ⋮ 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 ⋮ Bootstrapping and sample splitting for high-dimensional, assumption-lean inference ⋮ Wasserstein distance error bounds for the multivariate normal approximation of the maximum likelihood estimator

Cites Work

• Bounds for the normal approximation of the maximum likelihood estimator
• On the existence and uniqueness of the maximum likelihood estimate of a vector-valued parameter in fixed-size samples
• The asymptotic expansion of a ratio of gamma functions
• Normal Approximations with Malliavin Calculus
• Normal Approximation by Stein’s Method
• Asymptotics of Maximum Likelihood without the LLN or CLT or Sample Size Going to Infinity
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