Bernoulli

  • Bernoulli
  • Volume 5, Number 2 (1999), 315-331.

Asymptotic normality of posterior distributions in high-dimensional linear models

Subhashis Ghosal

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Abstract

We study consistency and asymptotic normality of posterior distributions of the regression coefficient in a linear model when the dimension of the parameter grows with increasing sample size. Under certain growth restrictions on the dimension (depending on the design matrix), we show that the posterior distributions concentrate in neighbourhoods of the true parameter and can be approximated by an appropriate normal distribution.

Article information

Source
Bernoulli, Volume 5, Number 2 (1999), 315-331.

Dates
First available in Project Euclid: 5 March 2007

Permanent link to this document
https://projecteuclid.org/euclid.bj/1173147909

Mathematical Reviews number (MathSciNet)
MR1681701

Zentralblatt MATH identifier
0948.62007

Keywords
high dimension linear model normal approximation posterior consistency posterior distribution

Citation

Ghosal, Subhashis. Asymptotic normality of posterior distributions in high-dimensional linear models. Bernoulli 5 (1999), no. 2, 315--331. https://projecteuclid.org/euclid.bj/1173147909


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