通过路径边际似然阈值进行模型选择

Pub Date : 2024-07-11 DOI:10.1016/j.spl.2024.110214

Claudia Di Caterina , Davide Ferrari

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引用次数: 0

摘要

我们建议通过从一个潜在的大集合中选择边际似然值来估计多元模型中的稀疏参数向量。由此产生的估计器涉及一种自适应阈值机制，根据边际估计值对沿着日益复杂的模型路径计算出的联合信息的顺序贡献，将边际估计值设为零。我们将通过模拟来说明我们建议的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Model selection by pathwise marginal likelihood thresholding

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.

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