Shared Bayesian variable shrinkage in multinomial logistic regression
From MaRDI portal
Publication:2084055
DOI10.1016/j.csda.2022.107568OpenAlexW4286253959MaRDI QIDQ2084055
Jeremy T. Gaskins, Md Nazir Uddin
Publication date: 17 October 2022
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2022.107568
Cites Work
- Unnamed Item
- A Bayesian analysis of the multinomial probit model using marginal data augmentation
- Achieving shrinkage in a time-varying parameter model framework
- A Bayesian analysis of the multinomial probit model with fully identified parameters
- The horseshoe+ estimator of ultra-sparse signals
- Fast Bayesian variable selection for high dimensional linear models: marginal solo spike and slab priors
- A novel Bayesian approach for variable selection in linear regression models
- Lasso meets horseshoe: a survey
- Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership
- Inference with normal-gamma prior distributions in regression problems
- The horseshoe estimator for sparse signals
- Mixtures of g Priors for Bayesian Variable Selection
- The Bayesian Lasso
- Bayesian Variable Selection in Linear Regression
- Hyper Markov Laws for Correlation Matrices
- EMVS: The EM Approach to Bayesian Variable Selection
- Bayesian variable selection for multioutcome models through shared shrinkage
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Dirichlet–Laplace Priors for Optimal Shrinkage
- Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables