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A semiparametric Bernstein–von Mises theorem for Gaussian process priors
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  • Published: 11 August 2010

A semiparametric Bernstein–von Mises theorem for Gaussian process priors

  • Ismaël Castillo1 

Probability Theory and Related Fields volume 152, pages 53–99 (2012)Cite this article

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Abstract

This paper is a contribution to the Bayesian theory of semiparametric estimation. We are interested in the so-called Bernstein–von Mises theorem, in a semiparametric framework where the unknown quantity is (θ, f), with θ the parameter of interest and f an infinite-dimensional nuisance parameter. Two theorems are established, one in the case with no loss of information and one in the information loss case with Gaussian process priors. The general theory is applied to three specific models: the estimation of the center of symmetry of a symmetric function in Gaussian white noise, a time-discrete functional data analysis model and Cox’s proportional hazards model. In all cases, the range of application of the theorems is investigated by using a family of Gaussian priors parametrized by a continuous parameter.

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Authors and Affiliations

  1. CNRS, LPMA Paris, 175, rue du Chevaleret, 75013, Paris, France

    Ismaël Castillo

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  1. Ismaël Castillo
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Correspondence to Ismaël Castillo.

Additional information

This work was partly supported by a postdoctoral fellowship from the Vrije Universiteit Amsterdam, the Netherlands.

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Cite this article

Castillo, I. A semiparametric Bernstein–von Mises theorem for Gaussian process priors. Probab. Theory Relat. Fields 152, 53–99 (2012). https://doi.org/10.1007/s00440-010-0316-5

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  • Received: 16 January 2009

  • Revised: 05 July 2010

  • Published: 11 August 2010

  • Issue Date: February 2012

  • DOI: https://doi.org/10.1007/s00440-010-0316-5

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Keywords

  • Bayesian non and semiparametrics
  • Bernstein–von Mises Theorems
  • Gaussian process priors
  • Estimation of the center of symmetry
  • Cox’s proportional hazards model

Mathematics Subject Classification (2000)

  • 62G05
  • 62G20
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