{
  "abstract": "Quantile regression (QR) provides a flexible framework for characterizing conditional distributions and can offer useful insights for quantile estimation. In this paper, we provide intuition for the interpretation of QR by highlighting the approach that it is a reparameterization of more popular conditional cumulative distribution function regression. From this approach, we naturally explore the use of the proportional hazards Cox regression model in the QR context. Therefore, we propose a framework based on the Cox regression model for estimating conditional quantiles, obtained through a reparameterization of the conditional cumulative distribution function under a proportional hazards structure. We establish the large-sample properties of the resulting estimator and investigate its behavior in finite samples through a Monte Carlo simulation study. An empirical application illustrates its practical implementation in a real-world context. Furthermore, we introduce the average quantile effect, a summary measure for quantifying the effects of covariates across conditional quantiles. The proposed estimator behaves adequately under the required assumptions, although inferences on the associated parameters strongly depend on these premises.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medicine, University of Oviedo, 33006 Oviedo, Spain",
    "Instituto Universitario de Oncología del Principado de Asturias, 33006 Oviedo, Spain",
    "CIBER of Epidemiology and Public Health, Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain",
    "Department of Statistics and Operations Research and Mathematics Didactics, University of Oviedo, 33007 Oviedo, Spain",
    "Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011 Oviedo, Spain",
    "Department of Anesthesiology, Geisel School of Medicine at Dartmouth, 7 Lebanon Street, Suite 309, Hinman Box 7261, Lebanon, NH 03751, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Rocío Fernández-Iglesias",
      "orcid": "0000-0001-7760-4994"
    },
    {
      "affs": [
        3
      ],
      "name": "Sonia Pérez-Fernández",
      "orcid": "0000-0002-2767-6399"
    },
    {
      "affs": [
        0,
        1,
        4
      ],
      "name": "Miguel García-Villarino",
      "orcid": "0000-0002-0186-2804"
    },
    {
      "affs": [
        5
      ],
      "name": "Pablo Martínez-Camblor",
      "orcid": "0000-0001-7845-3905"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dgjykbjsxa6wc.bib",
  "cited_by": 0,
  "doi": "10.3390/axioms15100768",
  "fulltext_url": null,
  "id": "dgjykbjsxa6wc",
  "issns": [
    "2075-1680"
  ],
  "landing_url": "https://www.mdpi.com/2075-1680/15/10/768",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7221757846",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/dgjykbjsxa6wc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:30Z",
      "native_id": "10.3390/axioms15100768",
      "source": "crossref",
      "url": "https://www.mdpi.com/2075-1680/15/10/768"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Statistical Methods and Inference",
      "subfield": "statistics"
    }
  ],
  "title": "A Cox-Based Framework for Quantile Estimation",
  "title_text": "A Cox-Based Framework for Quantile Estimation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:30Z",
  "url": "https://research.draconitic.com/paper/dgjykbjsxa6wc",
  "venue": "Axioms",
  "versions": []
}