Nonparametric Bernstein-von Mises theorems in Gaussian white noise (Q385781)

scientific article; zbMATH DE number 6237603

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English	Nonparametric Bernstein-von Mises theorems in Gaussian white noise	scientific article; zbMATH DE number 6237603

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instance of

scholarly article

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title

Nonparametric Bernstein-von Mises theorems in Gaussian white noise (English)

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The Annals of Statistics

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publication date

11 December 2013

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full work available at URL

https://arxiv.org/abs/1208.3862

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https://projecteuclid.org/euclid.aos/1382547511

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zbMATH Keywords

Bayesian inference

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plug-in property

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efficiency

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The semiparametric Bernstein-von Mises theorem

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Nonparametric estimators which can be ``plugged-in''.

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Uniformity in weak convergence

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On the small balls problem for equivalent Gaussian measures

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Bernstein-von Mises theorems for Gaussian regression with increasing number of regressors

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A Bernstein-von Mises theorem for discrete probability distributions

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A semiparametric Bernstein-von Mises theorem for Gaussian process priors

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Reference priors for exponential families with increasing dimension

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Wavelets on the interval and fast wavelet transforms

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An analysis of Bayesian inference for nonparametric regression

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Real Analysis and Probability

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Q4255465

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On the Bernstein-von Mises theorem with infinite-dimensional parameters

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Estimating Densities of Functions of Observations

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Asymptotic normality of posterior distributions in high-dimensional linear models

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Asymptotic normality of posterior distributions for exponential families when the number of parameters tends to infinity.

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Convergence rates of posterior distributions.

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Convergence rates of posterior distributions for non iid observations

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On local \(U\)-statistic processes and the estimation of densities of functions of several sample variables

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Uniform central limit theorems for kernel density estimators

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Uniform limit theorems for wavelet density estimators

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Rates of contraction for posterior distributions in \(L^{r}\)-metrics, \(1 \leq r \leq \infty\)

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Asymptotically minimax estimation of concave and convex distribution functions

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Bayesian inverse problems with Gaussian priors

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Efficient estimation of integral functionals of a density

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Asymptotic methods in statistical decision theory

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On the Bernstein-von Mises phenomenon in the Gaussian white noise model

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Erratum to: ``Donsker type theorems for nonparametric maximum likelihood estimators. Probab. Theory Relat. Fields 138, 411-449 (2007)''

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On convergence and convolutions of random signed measures

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Bernstein-von Mises theorem for linear functionals of the density

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Root<i>n</i>consistent density estimators for sums of independent random variables

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Rates of convergence of posterior distributions.

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Asymptotic Statistics

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Rates of contraction of posterior distributions based on Gaussian process priors

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Adaptive Bayesian estimation using a Gaussian random field with inverse gamma bandwidth

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Weak convergence and empirical processes. With applications to statistics

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Identifiers

zbMATH Open document ID

1285.62052

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DOI

10.1214/13-AOS1133

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:385781