Multivariate normal approximation of the maximum likelihood estimator via the delta method

DOI10.1214/18-BJPS411zbMath1441.62139arXiv1609.03970MaRDI QIDQ2180265

Robert E. Gaunt, Andreas Anastasiou

Publication date: 13 May 2020

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1609.03970

zbMATH Keywords

Stein's methodmultivariate normal distributionmulti-parameter maximum likelihood estimation

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Estimation in multivariate analysis (62H12)

Related Items (5)

Bounds in \(L^1\) Wasserstein distance on the normal approximation of general M-estimators ⋮ Improved bounds in Stein's method for functions of multivariate normal random vectors ⋮ 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

Cites Work

• Unnamed Item
• Rates of convergence in normal approximation under moment conditions via new bounds on solutions of the Stein equation
• 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
• Assessing the multivariate normal approximation of the maximum likelihood estimator from high-dimensional, heterogeneous data
• Bounds for the asymptotic normality of the maximum likelihood estimator using the Delta method
• Statistical Methods in Markov Chains

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