Abstract
We suggest to estimate a sparse parameter vector in multivariate models through the selection of marginal likelihoods from a potentially large set. The resulting estimator involves an adaptive thresholding mechanism, whereby the marginal estimates are set to zero according to their sequential contribution to the joint information computed along a path of increasingly complex models. The effectiveness of our proposal is illustrated via simulations.