ArticleStatistics and computing
Model-based clustering based on sparse finite Gaussian mixtures.
Article in Statistics and computing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A flexible class of latent variable models for the analysis of antibody response data.Biostatistics (Oxford, England) · 2026Article
- A unified MAP-EM approach to stable Gaussian mixture clustering with priors, graphs, and split-merge adaptation for document clustering.Frontiers in artificial intelligence · 2026Article
- AI-driven reclassification of multiple sclerosis progression.Nature medicine · 2025Article
- Article
- Product Centred Dirichlet Processes for Bayesian Multiview Clustering.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data.Bioinformatics advances · 2025Article
- Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.Genetic epidemiology · 2024Article
- Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses.Bayesian analysis · 2024Article
- A HORSESHOE MIXTURE MODEL FOR BAYESIAN SCREENING WITH AN APPLICATION TO LIGHT SHEET FLUORESCENCE MICROSCOPY IN BRAIN IMAGING.The annals of applied statistics · 2023Article
- A global perspective on the intrinsic dimensionality of COVID-19 data.Scientific reports · 2023Article
- Bayesian clustering of multiple zero-inflated outcomes.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Bayesian Mixture Model of Extended Redundancy Analysis.Psychometrika · 2022Article
- BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies.Genome biology · 2022Article
- How many data clusters are in the Galaxy data set?: Bayesian cluster analysis in action.Advances in data analysis and classification · 2022Article
- Dynamic Dirichlet process mixture model for identifying voting coalitions in the United Nations General Assembly human rights roll call votes.Journal of applied statistics · 2022Article
- Stock price reaction to the drug development setbacks in the pharmaceutical industry.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2021Article
- Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model.Annals of neurology · 2020Article
- Robust Clustering with Subpopulation-specific Deviations.Journal of the American Statistical Association · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using [Formula: see text]-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.