{
  "abstract": "This paper introduces a quantile-based Kumaraswamy-Teissier autoregressive moving average (KTARMA) model for positive-valued time series. Leveraging the flexibility of the extended Kumaraswamy-Teissier distribution within an observation-driven framework, the random component of the distribution is conditioned on the historical process and time-varying covariates, and is parameterized explicitly via its $ρ$-th conditional quantile, where $ρ\\in (0,1)$. To capture temporal dependence, the systematic component maps an ARMA-type structure to this conditional quantile via an appropriate link function. For inference, we implement a conditional maximum likelihood framework and derive explicit analytical expressions for the resulting score vector and conditional information matrix, followed by the development of model diagnostic and forecasting procedures. The finite-sample performance of the developed estimators is evaluated through a Monte Carlo simulation study across various parameter configurations and quantile levels. Finally, the practical utility of the study is demonstrated by modeling monthly rainfall data over the Northwest Himalayas (2001-2025), where 525 grids are grouped into four homogeneous zones using a Self-Organizing Map and relevant atmospheric variables and large-scale climate indices are incorporated as predictive regressors. Out-of-sample forecasting evaluations reveal that the KTARMA model delivers highly competitive predictive performance, achieving consistently lower mean squared errors across all identified zones compared to KARMA and $β$ARMA models.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.04736",
  "author_total": 3,
  "authors": [
    {
      "name": "Kamana Mishra"
    },
    {
      "name": "Tanmay Kayal"
    },
    {
      "name": "Sarita Azad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8k5xyxxhfqsqj.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.04736",
  "fulltext_url": null,
  "id": "8k5xyxxhfqsqj",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.04736",
  "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": "W7211889246",
  "pdf_url": "https://arxiv.org/pdf/2609.04736",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8k5xyxxhfqsqj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "2609.04736",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.04736"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "A Quantile-Based Kumaraswamy-Teissier autoregressive moving average models",
  "title_text": "A Quantile-Based Kumaraswamy-Teissier autoregressive moving average models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/8k5xyxxhfqsqj",
  "venue": "arXiv",
  "versions": []
}