Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior. Issue 17 (22nd November 2019)

• Record Type: Journal Article
• Authors: Yu, Yang; Zou, Zhihong; Wang, Shanshan; Meyer, Renate
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 17(2019)
• Page Start: 3290
• Page End: 3312
• Publication Date: 2019-11-22
• Subjects: Bernstein polynomial prior -- Dirichlet process -- Single-index model -- Binary indicator variable -- asymmetric Laplace distribution; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1663191
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.