{
  "abstract": "Multivariate longitudinal count data arise in biological studies, where responses may be over-dispersed, correlated within subjects and over time, measured at irregular times, and exhibit unobserved subgroup structure. Our goal was to develop a model that can accommodate over-dispersion, longitudinal dependence, cross-outcome dependence, covariate effects, irregular observation times, and covariate-dependent latent-class membership. We propose a latent-class Poisson-lognormal mixed model for clustering heterogeneous multivariate count trajectories. Estimation is performed using a variational EM algorithm designed for scalable inference. Simulation studies show accurate parameter estimation, strong latent-class recovery, and reliable selection of the number of classes, as compared to state-of-the-art approaches. We also applied the proposed approach to longitudinal neutrophil RNA-seq data from hospitalized COVID-19 patients as an illustration. The proposed framework provides a flexible approach for clustering complex longitudinal biological count data. Implementation code is available at https://github.com/PengqiLiu-Mc/latent-class-mpln-mixed-models.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.12860",
  "author_total": 3,
  "authors": [
    {
      "name": "Pengqi Liu"
    },
    {
      "name": "Utkarsh J. Dang"
    },
    {
      "name": "Sanjeena Subedi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fdeknd8m3ags5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.12860",
  "fulltext_url": null,
  "id": "fdeknd8m3ags5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.12860",
  "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",
      "1702|Bayesian Methods and Mixture Models"
    ]
  ],
  "openalex": "W7221532883",
  "pdf_url": "https://arxiv.org/pdf/2610.12860",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fdeknd8m3ags5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:46Z",
      "native_id": "2610.12860",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.12860"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data",
  "title_text": "Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:46Z",
  "url": "https://research.draconitic.com/paper/fdeknd8m3ags5",
  "venue": "arXiv",
  "versions": []
}