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Model selection by pathwise marginal likelihood thresholding

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  • Di Caterina, Claudia
  • Ferrari, Davide

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.

Suggested Citation

  • Di Caterina, Claudia & Ferrari, Davide, 2024. "Model selection by pathwise marginal likelihood thresholding," Statistics & Probability Letters, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:stapro:v:214:y:2024:i:c:s0167715224001834
    DOI: 10.1016/j.spl.2024.110214
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