{
  "abstract": "Generalized Poisson regression is widely used for modeling overdispersed count data; however, existing nonparametric local linear generalized Poisson regression methods are generally restricted to a single predictor, limiting their applicability to datasets involving multiple predictors. This study extends the nonparametric local linear generalized Poisson regression framework to accommodate multiple predictors, namely multi-predictor local linear generalized Poisson regression (MP-LLGPR). The proposed method provides a flexible approach for modeling overdispersed count response variable with locally varying relationships involving multiple predictors. The method is empirically validated in a healthcare application using stunting count data from 44 regencies and cities in Kalimantan, Indonesia. The empirical validation demonstrates the applicability of the proposed method for modeling overdispersed count data with multiple predictors and provides a comparison with the corresponding global generalized Poisson regression model.",
  "abstract_shown": true,
  "affiliations": [
    "Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.",
    "Nutrition Study Program, Faculty of Public Health, Mulawarman University, Samarinda, Indonesia.",
    "Statistics Study Program, Department of Mathematics, Faculty of Sciences and Technology, Airlangga University, Surabaya, Indonesia.",
    "School of Industrial Technology, Universiti Sains Malaysia, 11800 Penang, Malaysia.",
    "Polytechnic of Statistics STIS, Jakarta, Indonesia.",
    "Biology Study Program, Department of Biology, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.",
    "Physics Study Program, Department of Physics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.",
    "Physics Study Program, Faculty of Mathematics and Natural Sciences, Palangka Raya University, Palangka Raya, Indonesia.",
    "Mathematics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia.",
    "Universitas Muhammadiyah Kalimantan Timur, Samarinda, Indonesia.",
    "Environmental Science Study Program, Department of Biology, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Darnah"
    },
    {
      "affs": [
        0
      ],
      "name": "M Fathurahman"
    },
    {
      "affs": [
        1
      ],
      "name": "Iriyani Kamaruddin"
    },
    {
      "affs": [
        0
      ],
      "name": "Suyitno"
    },
    {
      "affs": [
        2
      ],
      "name": "Toha Saifudin"
    },
    {
      "affs": [
        3
      ],
      "name": "Mardiana Idayu Ahmad"
    },
    {
      "affs": [
        4
      ],
      "name": "Erni Tri Astuti"
    },
    {
      "affs": [
        5
      ],
      "name": "Ratna Kusuma"
    },
    {
      "affs": [
        6
      ],
      "name": "Rahmawati Munir"
    },
    {
      "affs": [
        7
      ],
      "name": "Made Dirgantara"
    },
    {
      "affs": [
        8
      ],
      "name": "Fidia Deny Tisna Amijaya"
    },
    {
      "affs": [
        9
      ],
      "name": "Imam Rofi'i"
    },
    {
      "affs": [
        10
      ],
      "name": "Andi Syaifullah Al Maslulil Agri"
    },
    {
      "affs": [
        0
      ],
      "name": "Feni Arista Bahni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fwe8e9nnnjywp.bib",
  "cited_by": 0,
  "doi": "10.1016/j.mex.2026.104228",
  "fulltext_url": "https://europepmc.org/articles/PMC13744270",
  "id": "fwe8e9nnnjywp",
  "issns": [
    "2215-0161"
  ],
  "landing_url": "https://europepmc.org/article/MED/42999864",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2613|Statistical Methods and Bayesian Inference"
    ]
  ],
  "openalex": "W7223339143",
  "pdf_url": "https://europepmc.org/articles/PMC13744270?pdf=render",
  "pmcid": "PMC13744270",
  "pmid": "42999864",
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fwe8e9nnnjywp.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42999864",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42999864"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Statistical Methods and Bayesian Inference",
      "subfield": "statistics"
    }
  ],
  "title": "A multi-predictor nonparametric local linear generalized Poisson regression model for overdispersed count data.",
  "title_text": "A multi-predictor nonparametric local linear generalized Poisson regression model for overdispersed count data.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:34Z",
  "url": "https://research.draconitic.com/paper/fwe8e9nnnjywp",
  "venue": "MethodsX",
  "versions": []
}