Adaptive Ridge Selector (ARiS)

TL;DR

The paper introduces the adaptive ridge selector (ARiS), a Bayesian-inspired variable selection method for linear models that improves prediction and model selection accuracy, especially in sparse data scenarios.

Contribution

It develops a novel Bayesian hierarchical approach for variable selection, extending the relevance vector machine with a new prior and an empirical Bayes hyperparameter tuning method.

Findings

ARiS outperforms lasso and ridge in sparse settings

Significant improvement in model selection accuracy with larger sample sizes

Demonstrates superior prediction performance in simulated data

Abstract

We introduce a new shrinkage variable selection operator for linear models which we term the \emph{adaptive ridge selector} (ARiS). This approach is inspired by the \emph{relevance vector machine} (RVM), which uses a Bayesian hierarchical linear setup to do variable selection and model estimation. Extending the RVM algorithm, we include a proper prior distribution for the precisions of the regression coefficients, $v_{j}^{-1} \sim f(v_{j}^{-1}|\eta)$, where $\eta$ is a scalar hyperparameter. A novel fitting approach which utilizes the full set of posterior conditional distributions is applied to maximize the joint posterior distribution $p(\boldsymbol\beta,\sigma^{2},\mathbf{v}^{-1}|\mathbf{y},\eta)$ given the value of the hyper-parameter $\eta$. An empirical Bayes method is proposed for choosing $\eta$. This approach is contrasted with other regularized least squares estimators including the lasso, its variants, nonnegative garrote and ordinary ridge regression. Performance differences are explored for various simulated data examples. Results indicate superior prediction and model selection accuracy under sparse setups and drastic improvement in accuracy of model choice with increasing sample size.