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• W1949167512 abstract "Feature selection is demanded in many modern scientific research problems that use high-dimensional data. A typical example is to find the most useful genes that are related to a certain disease (eg, cancer) from high-dimensional gene expressions. The expressions of genes have grouping structures, for example, a group of co-regulated genes that have similar biological functions tend to have similar expressions. In this paper, we propose to use a sophisticated Markov chain Monte Carlo method (Hamiltonian Monte Carlo in restricted Gibbs sampling) to fit Robit regression with heavy-tailed priors to make selection in the features with grouping structure. We will refer to this method as fully Bayesian Robit with Heavy-tailed prior (shortened by FBRHT). The main features of FBRHT are that it discards more aggressively unrelated features than LASSO, and also makes feature selection within groups automatically without a pre-specified grouping structure. Consequently, the feature subsets selected by FBRHT are significantly more sparse with only one representative from each group retained in the selected subset. In this paper, we use simulated and real datasets to demonstrate this within-group selection property of heavy-tailed priors. In general, we do not expect that such succinct feature subsets have better predictive power than the subsets of much larger size. Nevertheless, we will see that the predictive power of the sparse feature subsets selected by FBRHT are comparable with other much larger feature subsets selected by LASSO, group LASSO, supervised group LASSO, penalized logistic regression and random forest. In addition, we will show that the succinct feature subsets selected by FBRHT have significantly better predictive power than the feature subsets of the same size taken from the top features selected by the aforementioned methods." @default.
• W1949167512 created "2016-06-24" @default.
• W1949167512 creator A5045784932 @default.
• W1949167512 date "2015-10-08" @default.
• W1949167512 modified "2023-09-27" @default.
• W1949167512 title "Fully Bayesian T-probit Regression with Heavy-tailed Priors for Selection in High-Dimensional Features with Grouping Structure" @default.
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