HTLR-package: Bayesian Logistic Regression with Heavy-Tailed Priors

In longhaiSK/HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors

Bayesian Logistic Regression with Heavy-Tailed Priors

Description

Efficient Bayesian multinomial logistic regression based on heavy-tailed priors. This package is suitable for classification and feature selection with high-dimensional features, such as gene expression profiles. Heavy-tailed priors can impose stronger shrinkage (compared to Gaussian and Laplace priors) to the coefficients associated with a large number of useless features, but still allow coefficients of a small number of useful features to stand out without punishment. It can also automatically make selection within a large number of correlated features. The posterior of coefficients and hyper- parameters is sampled with restricted Gibbs sampling for leveraging high-dimensionality and Hamiltonian Monte Carlo for handling high-correlations among coefficients.

Author(s)

Maintainer: Longhai Li [e-mail] (ORCID)

Authors:

• Steven Liu [e-mail]

References

Longhai Li and Weixin Yao (2018). Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection. Journal of Statistical Computation and Simulation 2018, 88:14, 2827-2851.

See Also

Useful links:

• https://longhaisk.github.io/HTLR/

• Report bugs at https://github.com/longhaiSK/HTLR/issues

longhaiSK/HTLR documentation built on Nov. 15, 2024, 10:16 a.m.