The Annals of Statistics
- Ann. Statist.
- Volume 42, Number 1 (2014), 352-381.
Anisotropic function estimation using multi-bandwidth Gaussian processes
In nonparametric regression problems involving multiple predictors, there is typically interest in estimating an anisotropic multivariate regression surface in the important predictors while discarding the unimportant ones. Our focus is on defining a Bayesian procedure that leads to the minimax optimal rate of posterior contraction (up to a log factor) adapting to the unknown dimension and anisotropic smoothness of the true surface. We propose such an approach based on a Gaussian process prior with dimension-specific scalings, which are assigned carefully-chosen hyperpriors. We additionally show that using a homogenous Gaussian process with a single bandwidth leads to a sub-optimal rate in anisotropic cases. Zanten (2009) showed that rescaling a homogeneous smooth
Ann. Statist., Volume 42, Number 1 (2014), 352-381.
First available in Project Euclid: 19 March 2014
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Bhattacharya, Anirban; Pati, Debdeep; Dunson, David. Anisotropic function estimation using multi-bandwidth Gaussian processes. Ann. Statist. 42 (2014), no. 1, 352--381. doi:10.1214/13-AOS1192. https://projecteuclid.org/euclid.aos/1395234981