Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

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.

KEYWORDS:

• Bernstein polynomial prior
• Dirichlet process
• Single-index model
• Binary indicator variable
• asymmetric Laplace distribution

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Deutsche Forschungsgemeinschaft [AZ KI 1443/3-2] and National Natural Science Foundation of China [11701023,51478025] and Royal Society of New Zealand [JCF-UOA8101].