Semiparametric Bernstein-von Mises theorem and bias, illustrated with Gaussian process priors

DOI10.1007/s13171-012-0008-6zbMath1281.62087OpenAlexW4300077042MaRDI QIDQ2392501

Ismaël Castillo

Publication date: 1 August 2013

Published in: Sankhyā. Series A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13171-012-0008-6

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Parametric tolerance and confidence regions (62F25) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Bayesian inference (62F15)

Related Items (8)

On some aspects of the asymptotic properties of Bayesian approaches in nonparametric and semiparametric models ⋮ A Bernstein-von Mises theorem for smooth functionals in semiparametric models ⋮ Weak convergence of posteriors conditional on maximum pseudo-likelihood estimates and implications in ABC ⋮ Semiparametric Bernstein-von Mises for the error standard deviation ⋮ The semiparametric Bernstein-von Mises theorem ⋮ Efficient semiparametric estimation and model selection for multidimensional mixtures ⋮ Bayesian variance estimation in the Gaussian sequence model with partial information on the means ⋮ Frequentist Consistency of Variational Bayes

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