Bernstein von Mises Theorems for Gaussian Regression with increasing number of regressors

Article, Preprint, Other literature type English OPEN
Bontemps, Dominique;
(2011)
  • Publisher: Institute of Mathematical Statistics
  • Journal: issn: 0090-5364
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.1214/11-AOS912
  • Subject: 62J05 | semiparametric | [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] | [ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST] | nonparametric | Bernstein-von Mises Theorem | Mathematics - Statistics Theory | posterior asymptotic normality | 62G20 | Nonparametric Bayesian statistics | Bayesian Statistics | [ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH] | Bernstein–von Mises theorem | semiparametric Bayesian statistics | [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] | adaptive estimation | 62F15
    arxiv: Statistics::Methodology | Statistics::Computation | Statistics::Theory

International audience; This paper brings a contribution to the Bayesian theory of nonparametric and semiparametric estimation. We are interested in the asymptotic normality of the posterior distribution in Gaussian linear regression models when the number of regressors... View more
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