Electronic Journal of Statistics

Data-driven priors and their posterior concentration rates

Ryan Martin and Stephen G. Walker

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In high-dimensional problems, choosing a prior distribution such that the corresponding posterior has desirable practical and theoretical properties can be challenging. This begs the question: can the data be used to help choose a prior? In this paper, we develop a general strategy for constructing a data-driven or empirical prior and sufficient conditions for the corresponding posterior distribution to achieve a certain concentration rate. The idea is that the prior should put sufficient mass on parameter values for which the likelihood is large. An interesting byproduct of this data-driven centering is that the asymptotic properties of the posterior are less sensitive to the prior shape which, in turn, allows users to work with priors of computationally convenient forms while maintaining the desired rates. General results on both adaptive and non-adaptive rates based on empirical priors are presented, along with illustrations in density estimation, nonparametric regression, and high-dimensional normal models.

Article information

Electron. J. Statist., Volume 13, Number 2 (2019), 3049-3081.

Received: June 2018
First available in Project Euclid: 20 September 2019

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62C12: Empirical decision procedures; empirical Bayes procedures 62E20: Asymptotic distribution theory
Secondary: 62G07: Density estimation 62G08: Nonparametric regression

Adaptation data-dependent prior density estimation empirical Bayes nonparametric regression

Creative Commons Attribution 4.0 International License.


Martin, Ryan; Walker, Stephen G. Data-driven priors and their posterior concentration rates. Electron. J. Statist. 13 (2019), no. 2, 3049--3081. doi:10.1214/19-EJS1600. https://projecteuclid.org/euclid.ejs/1568944885

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