@article{mckay curtis_banerjee_ghosal_2014, title={Fast Bayesian model assessment for nonparametric additive regression}, volume={71}, ISSN={0167-9473}, url={http://dx.doi.org/10.1016/J.CSDA.2013.05.012}, DOI={10.1016/j.csda.2013.05.012}, abstractNote={Variable selection techniques for the classical linear regression model have been widely investigated. Variable selection in fully nonparametric and additive regression models has been studied more recently. A Bayesian approach for nonparametric additive regression models is considered, where the functions in the additive model are expanded in a B-spline basis and a multivariate Laplace prior is put on the coefficients. Posterior probabilities of models defined by selection of predictors in the working model are computed, using a Laplace approximation method. The prior times the likelihood is expanded around the posterior mode, which can be identified with the group LASSO, for which a fast computing algorithm exists. Thus Markov chain Monte-Carlo or any other time consuming sampling based methods are completely avoided, leading to quick assessment of various posterior model probabilities. This technique is applied to the high-dimensional situation where the number of parameters exceeds the number of observations.}, journal={Computational Statistics & Data Analysis}, publisher={Elsevier BV}, author={McKay Curtis, S. and Banerjee, Sayantan and Ghosal, Subhashis}, year={2014}, month={Mar}, pages={347–358} }