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

variable selection into a research topic of interest both in academia and in industry. The use of redundant, irrelevant, and … data collection. In this paper, we present a literature review on recent variable selection methods and applications in … manufacturing and in the chemometrics field. These methods are deployed into two major categories: variable selection for prediction …

structure determination in the SICRM; that is, the penalized estimating equations (PEE) for variable selection that combines the …

The following article considers a mixture of regressions with variable selection problem. In many real-data scenarios …

reparameterisation in non-centred form, the empirical evidence supporting a particular hypothesis is obtained by performing variable … selection on the model components, using a suitably designed Gibbs sampling scheme. The methodology is illustrated with …

An efficient estimating equations procedure is developed for performing variable selection and defining semiparametric …-index models. And this estimating equations procedure can be used to perform variable selection without solving any convex …

Variable selection techniques for the classical linear regression model have been widely investigated. Variable … selection in fully nonparametric and additive regression models has been studied more recently. A Bayesian approach for …

; however, methods for model selection are still very limited. A stochastic search variable selection (SSVS) approach under the … trial data set, for which the variable selection problem was previously debated upon in the literature. …