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Fully Bayesian logistic regression with hyper-LASSO priors for high-dimensional feature selection
2018
Journal of Statistical Computation and Simulation
High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g. cancer and normal) from tens of thousands of genes that are active (expressed) in certain tissue cells. To this end, we wish to fit regression and classification models with a large number of features (also called variables, predictors). In the past decade, penalized likelihood methods for fitting
doi:10.1080/00949655.2018.1490418
fatcat:cso4m5gqxrfsnazp7gfhtkepcq