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Clustering and variable selection for categorical multivariate data

https://doi.org/10.1214/13-ejs844
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/(sici)1099-0747(199709/12)13:3/4<269::aid-asm321>3.0.co;2-7openalex-snapshot
Clustering for binary data and mixture models—choice of the model
inferredcites →https://doi.org/10.1002/0471264385.wei0226openalex-snapshot
Latent Class and Latent Transition Analysis
inferredcites →https://doi.org/10.1002/0471721182openalex-snapshot
Finite Mixture Models
inferredcites →https://doi.org/10.1007/978-3-540-48503-2openalex-snapshot
Concentration Inequalities and Model Selection
inferredcites →https://doi.org/10.1007/bf02616237openalex-snapshot
Clustering criteria for discrete data and latent class models
inferredcites →https://doi.org/10.1007/s00440-006-0011-8openalex-snapshot
Minimal Penalties for Gaussian Model Selection
inferredcites →https://doi.org/10.1007/s10592-005-9098-1openalex-snapshot
Relative performance of Bayesian clustering software for inferring population substructure and individual assignment at low levels of population differentiation
inferredcites →https://doi.org/10.1007/s11634-009-0043-xopenalex-snapshot
Variable selection in model-based clustering using multilocus genotype data
inferredcites →https://doi.org/10.1016/j.ipm.2006.11.001openalex-snapshot
Inference and evaluation of the multinomial mixture model for text clustering
inferredcites →https://doi.org/10.1016/s0378-3758(98)00168-2openalex-snapshot
Model selection with data-oriented penalty
inferredcites →https://doi.org/10.1051/ps/2009004openalex-snapshot
A non asymptotic penalized criterion for Gaussian mixture model selection
inferredcites →https://doi.org/10.1051/ps/2010002openalex-snapshot
Data-driven penalty calibration: A case study for Gaussian mixture model selection
inferredcites →https://doi.org/10.1080/01621459.2000.10474285openalex-snapshot
Computational and Inferential Difficulties with Mixture Posterior Distributions
inferredcites →https://doi.org/10.1093/biomet/61.2.215openalex-snapshot
Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models
inferredcites →https://doi.org/10.1093/genetics/155.2.945openalex-snapshot
Inference of Population Structure Using Multilocus Genotype Data
inferredcites →https://doi.org/10.1093/genetics/159.2.699openalex-snapshot
Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds
inferredcites →https://doi.org/10.1109/34.865189openalex-snapshot
Assessing a mixture model for clustering with the integrated completed likelihood
inferredcites →https://doi.org/10.1111/j.1471-8286.2006.01527.xopenalex-snapshot
fastruct : model‐based clustering made faster
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot
Maximum Likelihood from Incomplete Data Via the EM Algorithm
inferredcites →https://doi.org/10.1186/1471-2105-9-539openalex-snapshot
Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations
inferredcites →https://doi.org/10.1214/aos/1015956709openalex-snapshot
Rates of convergence for the Gaussian mixture sieve
inferredcites →https://doi.org/10.4135/9781412984713openalex-snapshot
Latent Class Analysis
inferredcites →https://doi.org/10.48550/arxiv.0802.0837openalex-snapshot
Data-driven calibration of penalties for least-squares regression

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/sam.11502openalex-snapshot
Model‐based clustering of time‐dependent categorical sequences with application to the analysis of major life event patterns
inferred← citeshttps://doi.org/10.1007/s00357-018-9301-yopenalex-snapshot
Variable Selection for Mixed Data Clustering: Application in Human Population Genomics
inferred← citeshttps://doi.org/10.1007/s10618-019-00635-1openalex-snapshot
Efficient mixture model for clustering of sparse high dimensional binary data
inferred← citeshttps://doi.org/10.1007/s11634-024-00592-8openalex-snapshot
Comparison of internal evaluation criteria in hierarchical clustering of categorical data
inferred← citeshttps://doi.org/10.1016/j.jmp.2017.11.002openalex-snapshot
A hierarchical Bayesian approach for examining heterogeneity in choice decisions
inferred← citeshttps://doi.org/10.1016/j.neuroimage.2020.117431openalex-snapshot
Detecting brain network communities: Considering the role of information flow and its different temporal scales
inferred← citeshttps://doi.org/10.1093/jrsssb/qkae002openalex-snapshot
Full-model estimation for non-parametric multivariate finite mixture models
inferred← citeshttps://doi.org/10.1214/17-aoas1061openalex-snapshot
Variable selection for latent class analysis with application to low back pain diagnosis
inferred← citeshttps://doi.org/10.1214/18-ss119openalex-snapshot
Variable selection methods for model-based clustering
inferred← citeshttps://doi.org/10.1371/journal.pone.0180059openalex-snapshot
Diversity of inland valleys and opportunities for agricultural development in Sierra Leone
inferred← citeshttps://doi.org/10.3150/14-bej631openalex-snapshot
Nonparametric finite translation hidden Markov models and extensions
inferred← citeshttps://doi.org/10.4236/ojs.2017.72013openalex-snapshot
Clustering Categorical Data Based on Within-Cluster Relative Mean Difference
inferred← citeshttps://doi.org/10.48550/arxiv.1501.04787openalex-snapshot
Minimax adaptive estimation of nonparametric hidden Markov models
inferred← citeshttps://doi.org/10.48550/arxiv.1703.02293openalex-snapshot
Variable selection for mixed data clustering: a model-based approach
inferred← citeshttps://doi.org/10.48550/arxiv.1709.02294openalex-snapshot
Clustering and Model Selection via Penalized Likelihood for Different-sized Categorical Data Vectors
inferred← citeshttps://doi.org/10.48550/arxiv.1901.07277openalex-snapshot
Minimal penalties and the slope heuristics: a survey
inferred← citeshttps://doi.org/10.51936/lxut1974openalex-snapshot
Internal evaluation criteria for categorical data in hierarchical clustering
inferred← citeshttps://doi.org/10.70675/266dac9cza83dz4812z9cf2z39b2d31dfd4fopenalex-snapshot
Dimension reduction in logistic regression, application to actu-palu data
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