{
  "abstract": "This study proposes two novel bivariate distributions for jointly modeling temperature and rainfall by integrating Kumaraswamy-Teissier marginals with Clayton and Gumbel copula structures. To capture a wide range of dependence patterns, including both positive and negative associations, rotated copula variants (90°, 180°, and 270°) are incorporated along with their corresponding tail dependence characteristics. Model parameters are estimated using maximum likelihood and the inference functions for margins (IFM) approach, and their finite-sample performance is assessed through a comprehensive Monte Carlo simulation study. The proposed models are applied to monthly gridded temperature and rainfall data from the Northwest Himalayas, a region characterized by complex hydro-climatic variability. Comparative analysis demonstrates that the proposed framework outperforms several existing bivariate models and effectively captures lower-tail, upper-tail, and asymmetric dependence structures across summer and winter seasons. Based on the selected best-fitting copula models, univariate, joint, and conditional return periods are derived to quantify the risk of compound extremes. The results highlight the capability of the proposed approach to provide a more realistic representation of hydro-climatic dependence and offer a robust framework for assessing the risk of extreme temperature and rainfall events in mountainous regions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.04740",
  "author_total": 3,
  "authors": [
    {
      "name": "Kamana Mishra"
    },
    {
      "name": "Tanmay Kayal"
    },
    {
      "name": "Sarita Azad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bdbtmkzdd5ayk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.04740",
  "fulltext_url": null,
  "id": "bdbtmkzdd5ayk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.04740",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Distribution Estimation and Applications"
    ]
  ],
  "openalex": "W7211866296",
  "pdf_url": "https://arxiv.org/pdf/2609.04740",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bdbtmkzdd5ayk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "2609.04740",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.04740"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Copula-Based Bivariate Kumaraswamy-Teissier Distributions: Modeling Temperature-Rainfall Dependence and Compound Extremes",
  "title_text": "Copula-Based Bivariate Kumaraswamy-Teissier Distributions: Modeling Temperature-Rainfall Dependence and Compound Extremes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/bdbtmkzdd5ayk",
  "venue": "arXiv",
  "versions": []
}