{
  "abstract": "Modern statistical learning problems often involve multiple related data sets, in which learning efficiency on a target set can be improved by borrowing information from related sources, while source heterogeneity may introduce bias. Existing approaches are limited by suboptimal performance in multi-source settings, insufficient use of covariance information, or the computational burden of tuning procedures. We propose a covariance-aware shrinkage framework that constructs shrinkage directions using covariance information to improve efficiency. We establish finite-sample risk bounds that yield an explicit risk-improving interval for the shrinkage size, making the transfer strength fully data-driven. When multiple source sets are available, we further propose a sequential algorithm that applies source-specific shrinkage updates according to a priority score. Under a mixed-source separation condition, the estimator asymptotically attains the oracle risk and, when the pooled discrepancy is large, strictly improves over single-step shrinkage. The framework is further extended to a broad class of smooth M-estimation problems. Numerical studies show substantial gains over competing methods, especially when the source data sets are highly heterogeneous.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.30615",
  "author_total": 5,
  "authors": [
    {
      "name": "Wenbo Jing"
    },
    {
      "name": "Xi Chen"
    },
    {
      "name": "Yaqi Duan"
    },
    {
      "name": "Kaizheng Wang"
    },
    {
      "name": "Yichen Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2d654n4j549dh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.30615",
  "fulltext_url": null,
  "id": "2d654n4j549dh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.30615",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7166669347",
  "pdf_url": "https://arxiv.org/pdf/2606.30615",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2d654n4j549dh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:33Z",
      "native_id": "2606.30615",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.30615"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Tuning-Free Covariance-Aware Sequential Shrinkage for Multi-Source Estimation",
  "title_text": "Tuning-Free Covariance-Aware Sequential Shrinkage for Multi-Source Estimation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:33Z",
  "url": "https://research.draconitic.com/paper/2d654n4j549dh",
  "venue": "arXiv",
  "versions": []
}