Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data

Pengqi Liu; Utkarsh J. Dang; Sanjeena Subedi

Home/Archive/arXiv

arXiv · 2610.12860

Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data

Pengqi Liu·Utkarsh J. Dang·Sanjeena Subedi

View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

stat.ME

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Pengqi Liu, Utkarsh J. Dang, Sanjeena Subedi. 2026-10-09. Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data. https://arxiv.org/abs/2610.12860

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Debiased Estimating Equation Method for Robust and Efficient Mendelian Randomization Using a Large Number of Correlated Weak and Invalid Instruments

Mendelian randomization (MR) is a widely used tool for causal inference in the presence of unmeasured confounders, which uses single nucleotide polymorphisms (SNPs) as instrumental variables to estimate causal effects. However, SNPs often have weak effects on complex traits, leading to bias in existing MR analyses when weak instruments are included. In addition, existing MR methods often restrict analysis to independent SNPs via linkage disequilibrium clumping and result in a loss of efficiency in estimating the causal effect due to discarding correlated SNPs. To address these issues, we propose the Debiased Estimating Equation Method (DEEM), a summary statistics-based MR approach that can incorporate a large number of correlated, weak-effect, and invalid SNPs. DEEM effectively eliminates the weak instrument bias and improves the statistical efficiency of the causal effect estimation by leveraging information from a large number of correlated SNPs. DEEM also allows for pleiotropic effects, adjusts for the winner's curse, and applies to both two-sample and one-sample MR analyses. Asymptotic analyses of the DEEM estimator demonstrate its attractive theoretical properties. Through extensive simulations and two real data examples, we demonstrate that DEEM significantly improves the efficiency and robustness of MR analysis compared with existing methods.

stat.ME↗

arXiv2026

Latent Space Graph-fused LASSO for Clustering in Attributed Graphs

This manuscript studies latent space graph-fused LASSO methods for clustering graphs with multivariate nodal attributes. The framework includes node-specific priors of low-dimensional representations coupled with a neural decoder that bridges the observed attributes with the latent variables. Both structural and attribute information are incorporated for the clustering through a graph-fused LASSO regularization on prior nodal means. The optimization problem is solved via the alternating direction method of multipliers with Langevin dynamics for posterior inference. Simulations on grid and block graphs, together with applications to California county temperatures and Los Angeles traffic data, demonstrate the effectiveness of the proposed method.

stat.ME↗

arXiv2026

Missing data and cluster graphs: cluster-level missingness vs variable-level missingness

Missing data is pervasive in many scientific domains such as public health, environmental science, and the social sciences. Recoverability from missing data is typically studied using fully specified variable-level missingness models despite that, in many applications, only coarse structural information is available, for instance when variables are grouped into clusters due to limited knowledge or interpretability reasons. In this paper, we investigate recoverability from such abstract representations. We introduce two classes of cluster-based missingness graphs: the m-C-DMG, which retains variable-specific missingness indicators, and the cm-C-DMG, which aggregates missingness mechanisms at the cluster level. We formalize the notion of compatibility between these abstract graphs and underlying variable-level missingness models, and study how this abstraction affects the recoverability of probabilistic and causal queries. In particular, we give graphical conditions of recovering the joint distribution as well as graphical conditions of recovering a macro causal effect.

stat.ME↗