Variable selection in finite mixture of regression models with an unknown number of components
From MaRDI portal
Publication:830075
DOI10.1016/j.csda.2021.107180OpenAlexW3125731161MaRDI QIDQ830075
Kuo-Jung Lee, Yi-Chi Chen, Martin Feldkircher
Publication date: 7 May 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2021.107180
high-dimensional datafinancial crisisBayesian variable selectionfinite mixture of regression modelsunknown number of components
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Model-based clustering based on sparse finite Gaussian mixtures
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- The Adaptive Lasso and Its Oracle Properties
- \(\ell_{1}\)-penalization for mixture regression models
- Reversible jump and the label switching problem in hidden Markov models
- Consistent estimation of a mixing distribution
- A Bayesian mixture of Lasso regressions with \(t\)-errors
- Optimal predictive model selection.
- Finite mixture and Markov switching models.
- Regularization in Finite Mixture of Regression Models with Diverging Number of Parameters
- Sampling-Based Approaches to Calculating Marginal Densities
- Variable Selection in Regression Mixture Modeling for the Discovery of Gene Regulatory Networks
- Variable Selection in Finite Mixture of Regression Models
- Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Dealing With Label Switching in Mixture Models
- Finite mixture models
- A Statistical View of Some Chemometrics Regression Tools
- Model selection in finite mixture of regression models: a Bayesian approach with innovative weightedgpriors and reversible jump Markov chain Monte Carlo implementation
- A Thresholding Algorithm for Order Selection in Finite Mixture Models
- Testing the Order of a Finite Mixture
- Screening and clustering of sparse regressions with finite non‐Gaussian mixtures
- Regularization and Variable Selection Via the Elastic Net
- Bayesian Variable Selection in Clustering High-Dimensional Data
- Bayesian Variable Selection Under Collinearity
- A stochastic partitioning method to associate high-dimensional responses and covariates
This page was built for publication: Variable selection in finite mixture of regression models with an unknown number of components