selection stages using Markov chain Monte Carlo-based variable selection, before identifying the scale of latent variables. The …

The adaptive lasso is a model selection method shown to be both consistent in variable selection and asymptotically … normal in coefficient estimation. The actual variable selection performance of the adaptive lasso depends on the weight used … when collinearity of the model matrix is a concern. To achieve better variable selection results, we take into account the …

We consider the problem of variable selection for the generalized linear models (GLMs) with longitudinal data. An … automatic variable selection procedure is developed using smooth-threshold generalized estimating equations (SGEE). The proposed … existing variable selection methods: the resulting estimator enjoys the oracle property; the proposed procedure avoids the …

the model and propose corresponding thresholding estimators that can be used for variable selection. For each thresholding … estimator, variable selection in the linear component is developed and consistency of the variable selection procedure is shown …

variable selection and fits the multinomial logit model also when predictors are high-dimensional. Since in multi …-category models the effect of one predictor variable is represented by several parameters one has to distinguish between variable … selection and parameter selection. A special feature of the approach is that, in contrast to existing approaches, it selects …

of the effectiveness and the cost, conditional on the effectiveness. In this paper, we argue that the Bayesian variable … selection procedure should be developed using the bivariate distribution of the cost and the effectiveness, which is not the …

multivariate response problems. In addition, we incorporate a variable selection procedure. Simulation studies show the efficacy of …

A variable selection problem is analysed for use in Principal Component Analysis (PCA). In this case, the set of … problem, with no known prior references, has two further difficulties, in addition to that of the variable selection problem …

The problem of variable selection for linear regression in a high dimension model is considered. A new method, called …