Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models

TL;DR

This paper introduces an improved Bayesian P-splines framework that splits latent variables into two groups, applying asymmetric corrections to enhance estimation accuracy while maintaining fast, sampling-free inference.

Contribution

It proposes a novel splitting of latent variables in Laplacian-P-splines, allowing tailored asymmetric approximations for better accuracy in sparse information scenarios.

Findings

Enhanced accuracy for unpenalized parameters.

Maintains fast, sampling-free inference.

Demonstrated on ordinal survey data.

Abstract

Laplacian-P-splines (LPS) associate the P-splines smoother and the Laplace approximation in a unifying framework for fast and flexible inference under the Bayesian paradigm. Gaussian Markov field priors imposed on penalized latent variables and the Bernstein-von Mises theorem typically ensure a razor-sharp accuracy of the Laplace approximation to the posterior distribution of these variables. This accuracy can be seriously compromised for some unpenalized parameters, especially when the information synthesized by the prior and the likelihood is sparse. We propose a refined version of the LPS methodology by splitting the latent space in two subsets. The first set involves latent variables for which the joint posterior distribution is approached from a non-Gaussian perspective with an approximation scheme that is particularly well tailored to capture asymmetric patterns, while the posterior distribution for parameters in the complementary latent set undergoes a traditional treatment with Laplace approximations. As such, the dichotomization of the latent space provides the necessary structure for a separate treatment of model parameters, yielding improved estimation accuracy as compared to a setting where posterior quantities are uniformly handled with Laplace. In addition, the proposed enriched version of LPS remains entirely sampling-free, so that it operates at a computing speed that is far from reach to any existing Markov chain Monte Carlo approach. The methodology is illustrated on the additive proportional odds model with an application on ordinal survey data.