Bayesian Variable Selection in Clustering High-Dimensional Data
From MaRDI portal
Publication:5754841
DOI10.1198/016214504000001565zbMath1117.62433OpenAlexW2085573033MaRDI QIDQ5754841
Mahlet G. Tadesse, Marina Vannucci, Naijun Sha
Publication date: 20 August 2007
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1198/016214504000001565
Related Items (61)
Variable selection for Fisher linear discriminant analysis using the modified sequential backward selection algorithm for the microarray data ⋮ Testing the order of a normal mixture in mean ⋮ Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data ⋮ Modelling breaks and clusters in the steady states of macroeconomic variables ⋮ Variable selection in finite mixture of regression models with an unknown number of components ⋮ Hypothesis test for normal mixture models: the EM approach ⋮ Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler ⋮ Variable Selection for Clustering with Gaussian Mixture Models ⋮ A hierarchical Bayesian approach for examining heterogeneity in choice decisions ⋮ Mixture Models With a Prior on the Number of Components ⋮ Bayesian variable selection for finite mixture model of linear regressions ⋮ Unnamed Item ⋮ Survival prediction and variable selection with simultaneous shrinkage and grouping priors ⋮ Combining clustering of variables and feature selection using random forests ⋮ Simultaneous Semiparametric Estimation of Clustering and Regression ⋮ A graph Laplacian prior for Bayesian variable selection and grouping ⋮ Unifying data units and models in (co-)clustering ⋮ Variable selection in model-based clustering and discriminant analysis with a regularization approach ⋮ Space‐time areal mixture model: relabeling algorithm and model selection issues ⋮ Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data ⋮ Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics ⋮ Penalized model-based clustering ⋮ A Bayesian Framework for Statistical Inference from Gene Expression Data ⋮ Modelling the role of variables in model-based cluster analysis ⋮ Comparing consensus Monte Carlo strategies for distributed Bayesian computation ⋮ Penalized model-based clustering with unconstrained covariance matrices ⋮ Bayesian estimation of the latent dimension and communities in stochastic blockmodels ⋮ Clustering time-course microarray data using functional Bayesian infinite mixture model ⋮ Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters ⋮ Estimating sample mean under interval uncertainty and constraint on sample variance ⋮ Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions ⋮ Test for homogeneity in Hardy-Weinberg normal mixture model ⋮ A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data ⋮ Variable selection methods for model-based clustering ⋮ A simple method for screening variables before clustering microarray data ⋮ Variable selection in model-based clustering: a general variable role modeling ⋮ Bayesian binary kernel probit model for microarray based cancer classification and gene selection ⋮ Dissimilarity and similarity measures for comparing dendrograms and their applications ⋮ Model-based clustering based on sparse finite Gaussian mixtures ⋮ The Kullback information criterion for mixture regression models ⋮ Functional equation helps in finding affine-covariant probability distributions ⋮ Bayesian variable selection in clustering high-dimensional data with substructure ⋮ Sparse Bayesian hierarchical modeling of high-dimensional clustering problems ⋮ Integrative model-based clustering of microarray methylation and expression data ⋮ High-dimensional variable selection with the plaid mixture model for clustering ⋮ Pairwise Variable Selection for High-Dimensional Model-Based Clustering ⋮ Bayesian multinomial regression with class-specific predictor selection ⋮ Modeling the Association Between Clusters of SNPs and Disease Responses ⋮ Variable selection for model-based clustering using the integrated complete-data likelihood ⋮ Bayesian analysis of multivariate Gaussian hidden Markov models with an unknown number of regimes ⋮ Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression ⋮ Data reduction in classification: A simulated annealing based projection method ⋮ On two‐way Bayesian agglomerative clustering of gene expression data ⋮ Spatial regression and estimation of disease risks: A clustering‐based approach ⋮ Model selection in finite mixture of regression models: a Bayesian approach with innovative weightedgpriors and reversible jump Markov chain Monte Carlo implementation ⋮ Bayesian variable selection in clustering high-dimensional data via a mixture of finite mixtures ⋮ Inference for multivariate normal mixtures ⋮ Variable selection for mixed data clustering: application in human population genomics ⋮ Bayesian bi-clustering methods with applications in computational biology ⋮ A simple model‐based approach to variable selection in classification and clustering ⋮ An Integrative Bayesian Modeling Approach to Imaging Genetics
This page was built for publication: Bayesian Variable Selection in Clustering High-Dimensional Data
