{
  "abstract": "Statistical prediction methods typically require some form of fine-tuning of tuning parameter(s), with $K$-fold cross-validation as the canonical procedure. For ridge regression there exist numerous procedures, but common for all, including cross-validation, is that one single parameter is chosen for all future predictions. We propose instead to calculate a unique tuning parameter for each individual for which we wish to predict an outcome. This generates an individualized prediction by focusing on the vector of covariates of a specific individual. The focused ridge -- fridge -- procedure is introduced with a two-part contribution: 1) first we define an oracle tuning parameter minimizing the mean squared prediction error of a specific covariate vector, 2) then we propose to estimate this tuning parameter by using plug-in estimates of the regression coefficients and error variance parameter. The procedure is extended to logistic ridge regression by utilizing parametric bootstrap. For high-dimensional data, we propose to use ridge regression with cross-validation as the plug-in estimate, and simulations show that fridge gives smaller average prediction error than ridge with cross-validation for both simulated and real data. We illustrate the new concept for both linear and logistic regression models in two applications of personalized medicine: predicting individual risk and treatment response based on gene expression data. The method is implemented in the R package \"fridge\".",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.08481",
  "author_total": 2,
  "authors": [
    {
      "name": "Kristoffer Herland Hellton"
    },
    {
      "name": "Nils Lid Hjort"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bh6kwzk6b6e2m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.08481",
  "fulltext_url": null,
  "id": "bh6kwzk6b6e2m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.08481",
  "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": "W7221044666",
  "pdf_url": "https://arxiv.org/pdf/2610.08481",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bh6kwzk6b6e2m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:32Z",
      "native_id": "2610.08481",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.08481"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Fridge: focused fine-tuning of ridge regression for personalize predictions",
  "title_text": "Fridge: focused fine-tuning of ridge regression for personalize predictions",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:32Z",
  "url": "https://research.draconitic.com/paper/bh6kwzk6b6e2m",
  "venue": "arXiv",
  "versions": []
}