{"ID":2830513,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09257","arxiv_id":"2512.09257","title":"Debiased Bayesian Inference for High-dimensional Regression Models","abstract":"There has been significant progress in Bayesian inference based on sparsity-inducing (e.g., spike-and-slab and horseshoe-type) priors for high-dimensional regression models. The resulting posteriors, however, in general do not possess desirable frequentist properties, and the credible sets thus cannot serve as valid confidence sets even asymptotically. We introduce a novel debiasing approach that corrects the bias for the entire Bayesian posterior distribution. We establish a new Bernstein-von Mises theorem that guarantees the frequentist validity of the debiased posterior. We demonstrate the practical performance of our proposal through Monte Carlo simulations and two empirical applications in economics.","short_abstract":"There has been significant progress in Bayesian inference based on sparsity-inducing (e.g., spike-and-slab and horseshoe-type) priors for high-dimensional regression models. The resulting posteriors, however, in general do not possess desirable frequentist properties, and the credible sets thus cannot serve as valid co...","url_abs":"https://arxiv.org/abs/2512.09257","url_pdf":"https://arxiv.org/pdf/2512.09257v1","authors":"[\"Qihui Chen\",\"Zheng Fang\",\"Ruixuan Liu\"]","published":"2025-12-10T02:24:37Z","proceeding":"econ.EM","tasks":"[\"econ.EM\",\"math.ST\",\"stat.CO\",\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}