The penalized least squares approach with smoothly clipped absolute deviation penalty has been consistently demonstrated to be an attractive regression shrinkage and selection method. It not only automatically and consistently selects the important variables, but also produces estimators which...

Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for...

When functional data are not homogenous, for example, when there are multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this article, we propose a new estimation procedure for the mixture of Gaussian processes, to incorporate both functional and...

Supersaturated designs (SSDs) can save considerable cost in industrial experimentation when many potential factors are introduced in preliminary studies. Analyzing data in SSDs is challenging because the number of experiments is less than the number of candidate factors. In this paper, we...

In this paper we are concerned with Bayesian statistical inference for a class of elliptical distributions with parameters[mu]and[Sigma]. Under a noninformative prior distribution, we obtain the posterior distribution, posterior mean, and generalized maximim likelihood estimators...