We propose the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient estimation in linear regression … discuss other variants of this new approach and provide a unified framework for variable selection using flexible penalties …

studies. A common problem when implementing a variable selection algorithm in such settings is the requirement that the model …

Variable selection has been suggested for Random Forests to improve data prediction and interpretation. However, the … investigate the properties of these procedures when combined with two popular variable selection methods. Findings and … recommendations: Complete case analysis should not be used as it led to inaccurate variable selection. Multiple imputation is the …

variable selection strategy which combines the advantages of the componentwise Gibbs sampler (see Chen et al., 2011) and the …

features in the variable space. Many of the variable selection techniques that are commonly used alongside clustering … computationally expensive for high-dimensional data. In this paper, a novel variable selection technique is introduced for use in … comparable variable selection techniques on the real data sets. Copyright Springer Science+Business Media New York 2014 …

In this paper, we focus on the variable selection for semiparametric varying coefficient partially linear models with … longitudinal data. A new variable selection procedure is proposed based on the combination of the basis function approximations and … sample performance of the proposed variable selection procedure. We further illustrate the proposed procedure by an …