An Overview on the URV Model-Based Approach to Cluster Mixed-Type Data
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Publication:3296443
DOI10.1007/978-3-030-21140-0_5zbMath1436.62282OpenAlexW2971976458MaRDI QIDQ3296443
Publication date: 7 July 2020
Published in: Statistical Learning of Complex Data (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-21140-0_5
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30)
Uses Software
Cites Work
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- Latent class analysis variable selection
- Model-based clustering, classification, and discriminant analysis of data with mixed type
- Mixture of latent trait analyzers for model-based clustering of categorical data
- Extension of the mixture of factor analyzers model to incorporate the multivariate \(t\)-distribution
- Model-based clustering of high-dimensional data: a review
- Mixture models for mixed-type data through a composite likelihood approach
- A model-based approach to simultaneous clustering and dimensional reduction of ordinal data
- A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model
- A mixture of generalized latent variable models for mixed mode and heterogeneous data
- Latent class model with conditional dependency per modes to cluster categorical data
- Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler
- Mixture models for ordinal data: a pairwise likelihood approach
- Maximum likelihood estimation using composite likelihoods for closed exponential families
- A factor mixture analysis model for multivariate binary data
- 15 Item Response Theory in a General Framework
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