A Lasso-penalized BIC for mixture model selection

DOI10.1007/s11634-013-0155-1zbMath1474.62212arXiv1211.6451OpenAlexW2065708566MaRDI QIDQ2009036

Paul D. McNicholas, Sakyajit Bhattacharya

Publication date: 27 November 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1211.6451

zbMATH Keywords

model selectionmodel-based clusteringBICmixture modelsLasso

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical aspects of information-theoretic topics (62B10)

Related Items (7)

Variable diagnostics in model-based clustering through variation partition ⋮ Model-based clustering ⋮ Mixture model averaging for clustering ⋮ LASSO–penalized clusterwise linear regression modelling: a two–step approach ⋮ Variable selection methods for model-based clustering ⋮ A mixture of generalized hyperbolic factor analyzers ⋮ Mixtures of hidden truncation hyperbolic factor analyzers

Uses Software

• glasso

Cites Work

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• Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models
• Sparse inverse covariance estimation with the graphical lasso
• The Adaptive Lasso and Its Oracle Properties
• Bayesian regularization for normal mixture estimation and model-based clustering
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Estimating the dimension of a model
• Akaike's information criterion and recent developments in information complexity
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Extended Bayesian information criteria for model selection with large model spaces
• Variable Selection in Finite Mixture of Regression Models
• Statistical analysis of finite mixture distributions
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Sparsity and Smoothness Via the Fused Lasso
• Bayes Factors
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• A new look at the statistical model identification

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