Model selection by pathwise marginal likelihood thresholding

Claudia Di Caterina and Davide Ferrari

Statistics & Probability Letters, 2024, vol. 214, issue C

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.

Keywords: Composite likelihood; Independence likelihood; Pairwise likelihood; Multivariate analysis; Sparsity-inducing penalization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.spl.2024.110214

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