Adaptive estimation of multivariate functions using conditionally Gaussian tensor-product spline priors

R. Jonge, de, J.H. Zanten, van

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)
62 Downloads (Pure)

Abstract

We investigate posterior contraction rates for priors on multivariate functions that are constructed using tensor-product B-spline expansions. We prove that using a hierarchical prior with an appropriate prior distribution on the partition size and Gaussian prior weights on the B-spline coefficients, procedures can be obtained that adapt to the degree of smoothness of the unknown function up to the order of the splines that are used. We take a unified approach including important nonparametric statistical settings like density estimation, regression, and classification.
Original languageEnglish
Pages (from-to)1984-2001
JournalElectronic Journal of Statistics
Volume6
DOIs
Publication statusPublished - 2012

Fingerprint Dive into the research topics of 'Adaptive estimation of multivariate functions using conditionally Gaussian tensor-product spline priors'. Together they form a unique fingerprint.

Cite this