Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models

TL;DR

This paper introduces a spike-and-slab prior method for function selection in structured additive regression models, enabling flexible inclusion or exclusion of model components with improved MCMC convergence.

Contribution

We develop a novel spike-and-slab prior framework with multiplicative expansion for structured additive models, enhancing variable selection and model parsimony.

Findings

Effective in simulation studies and benchmark data

Demonstrates good mixing and convergence in MCMC

Applicable to complex real-world survival data

Abstract

Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects and further regression terms. The large flexibility of structured additive regression makes function selection a challenging and important task, aiming at (1) selecting the relevant covariates, (2) choosing an appropriate and parsimonious representation of the impact of covariates on the predictor and (3) determining the required interactions. We propose a spike-and-slab prior structure for function selection that allows to include or exclude single coefficients as well as blocks of coefficients representing specific model terms. A novel multiplicative parameter expansion is required to obtain good mixing and convergence properties in a Markov chain Monte Carlo simulation approach and is shown to induce desirable shrinkage properties. In simulation studies and with (real) benchmark classification data, we investigate sensitivity to hyperparameter settings and compare performance to competitors. The flexibility and applicability of our approach are demonstrated in an additive piecewise exponential model with time-varying effects for right-censored survival times of intensive care patients with sepsis. Geoadditive and additive mixed logit model applications are discussed in an extensive appendix.