Exploring dependence between categorical variables: benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms
DOI10.1016/j.jspi.2016.01.002zbMath1336.62145arXiv1401.7214OpenAlexW768707245WikidataQ36976791 ScholiaQ36976791MaRDI QIDQ254920
Michail Papathomas, Sylvia Richardson
Publication date: 8 March 2016
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1401.7214
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12) Contingency tables (62H17)
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- Unnamed Item
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Modelling Heterogeneity With and Without the Dirichlet Process
- Model-based clustering for conditionally correlated categorical data
- Tensor decompositions and sparse log-linear models
- The mode oriented stochastic search (MOSS) algorithm for log-linear models with conjugate priors
- Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
- Reversible jump methods for generalised linear models and generalised linear mixed models
- On a class of Bayesian nonparametric estimates: I. Density estimates
- On the correspondence from Bayesian log-linear modelling to logistic regression modelling with \(g\)-priors
- Model uncertainty
- Bayesian variable and link determination for generalised linear models
- A Bayesian analysis of some nonparametric problems
- Nonparametric Bayes inference on conditional independence
- A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data
- Nonparametric Bayes Conditional Distribution Modeling With Variable Selection
- A novel reversible jump algorithm for generalized linear models
- Bayesian profile regression with an application to the National survey of children's health
- Variable selection and dependency networks for genomewide data
- Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error
- Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes
- A fast procedure for model search in multidimensional contingency tables
- Bayesian Nonparametric Inference for Random Distributions and Related Functions
- High Dimensional Variable Selection via Tilting
- Gibbs Sampling Methods for Stick-Breaking Priors
- Annealing Markov Chain Monte Carlo with Applications to Ancestral Inference
- Simplex Factor Models for Multivariate Unordered Categorical Data
- Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models
- Nonparametric Bayes Modeling of Multivariate Categorical Data
- Shotgun Stochastic Search for “Largep” Regression
- Bayesian Factorizations of Big Sparse Tensors
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