{"ID":2880216,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14596","arxiv_id":"2508.14596","title":"Sequential Correct Screening and Post-Screening Inference","abstract":"Selecting the top-$m$ variables with the $m$ largest population parameters from a larger set of candidates is a fundamental problem in statistics. In this paper, we propose a novel methodology called Sequential Correct Screening (SCS), which sequentially screens out variables that are not among the top-$m$. A key feature of our method is its anytime validity; it provides a sequence of variable subsets that, with high probability, always contain the true top-$m$ variables. Furthermore, we develop a post-screening inference (PSI) procedure to construct confidence intervals for the selected parameters. Importantly, this procedure is designed to control the false coverage rate (FCR) whenever it is conducted -- an aspect that has been largely overlooked in the existing literature. We establish theoretical guarantees for both SCS and PSI, and demonstrate their performance through simulation studies and an application to a real-world dataset on suicide rates.","short_abstract":"Selecting the top-$m$ variables with the $m$ largest population parameters from a larger set of candidates is a fundamental problem in statistics. In this paper, we propose a novel methodology called Sequential Correct Screening (SCS), which sequentially screens out variables that are not among the top-$m$. A key featu...","url_abs":"https://arxiv.org/abs/2508.14596","url_pdf":"https://arxiv.org/pdf/2508.14596v1","authors":"[\"Masaki Toyoda\",\"Yoshimasa Uematsu\"]","published":"2025-08-20T10:27:23Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"math.ST\"]","methods":"[]","has_code":false}