to the sample size, a variable selection procedure based on adaptive Lasso is proposed to conduct estimation and variable … selection simultaneously. The procedure is shown to possess the oracle property, and is much superior to the adaptive Lasso …

In this paper, we investigate the variable selection problem for recurrent event data under the additive rate model …. It has a form similar to the ordinary least squares of a linear regression model up to a constant. We develop variable … selection procedures by penalizing the loss function with the adaptive L1 penalty and smoothly clipped absolute derivation …

In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile … regression is proposed. Existing approaches to variable selection in a binary classification context are sensitive to outliers …

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 …

the order of nd, where d is the dimension of X. Given the lack of smoothing, issues related to variable selection, the …

practical motivation for this problem is variable selection in a linear regression context. We want to sample from a Bayesian …