In this paper we are concerned with the problems of variable selection and estimation in double generalized linear … the tuning parameters, the consistency of the variable selection procedure and asymptotic properties of the resulting … of the proposed variable selection procedure, showing that the proposed variable selection method works satisfactorily …

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. …

Variable selection is a venerable problem in multivariate statistics. Simulated annealing is one of a variety of … metaheuristics that can be gainfully employed for variable selection; however, its effectiveness is influenced by algorithm design …

result on the necessity of variable selection under the sparsity condition is provided since the AUC of the classifier from …