Latent heterogeneity in COVID-19 hospitalisations: a cluster-weighted approach to analyse mortality

DOI10.1111/ANZS.12407MaRDI QIDQ6549263

Giorgio Vittadini, Daniele Spinelli, Paolo Berta, Salvatore Ingrassia

Publication date: 3 June 2024

Published in: Australian \& New Zealand Journal of Statistics (Search for Journal in Brave)

zbMATH Keywords

mortalitycluster-weighted modelsCOVID-19comorbiditieshospitalisations

Mathematics Subject Classification ID

Statistics (62-XX) Parametric inference (62Fxx) Multivariate analysis (62Hxx)

Cites Work

• Local statistical modeling via a cluster-weighted approach with elliptical distributions
• Clustering bivariate mixed-type data via the cluster-weighted model
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Choosing initial values for the EM algorithm for finite mixtures
• On Bernoulli's numerical solution of algebraic equations.
• Model-based clustering via linear cluster-weighted models
• Clustering and classification via cluster-weighted factor analyzers
• The generalized linear mixed cluster-weighted model
• Multivariate response and parsimony for Gaussian cluster-weighted models
• Multilevel cluster-weighted models for the evaluation of hospitals
• Multilevel logistic cluster‐weighted model for outcome evaluation in health care*
• Methods and Criteria for Model Selection

This page was built for publication: Latent heterogeneity in COVID-19 hospitalisations: a cluster-weighted approach to analyse mortality

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6549263)