Bayesian monotone regression using Gaussian process projection

Published

Journal Article

Shape-constrained regression analysis has applications in dose-response modelling, environmental risk assessment, disease screening and many other areas. Incorporating the shape constraints can improve estimation efficiency and avoid implausible results. We propose a novel method, focusing on monotone curve and surface estimation, which uses Gaussian process projections. Our inference is based on projecting posterior samples from the Gaussian process. We develop theory on continuity of the projection and rates of contraction. Our approach leads to simple computation with good performance in finite samples. The proposed projection method can also be applied to other constrained-function estimation problems, including those in multivariate settings. © 2014 Biometrika Trust.

Full Text

Duke Authors

Cited Authors

  • Lin, L; Dunson, DB

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 101 / 2

Start / End Page

  • 303 - 317

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/ast063

Citation Source

  • Scopus