The Lasso is an attractive approach to variable selection in sparse, highdimensional regression models. Much work has been done to study the selection and estimation prop[...]

Existing grouped variable selection methods rely heavily on prior group information, thus they may not be reliable if an incorrect group assignment is used. In this paper[...]

Generalized degrees of freedom measure the complexity of a modeling procedure; a modeling procedure is a combination of model selection and model fitting. In this manuscr[...]

2015-2016 UNCG University Libraries Open Access Publishing Fund Grant Winner. BackgroundCopy number variation (CNV) analysis has become one of the most important research[...]

In high-dimensional data settings where p » n, many penalized regularization approaches were studied for simultaneous variable selection and estimation. However, with the[...]

One fundamental ingredient of our work is to formally split the signals into strong and weak ones. The rationale is that the usual one-step method such as the least absol[...]

In high-dimensional settings, a penalized least squares approach may lose its efficiency in both estimation and variable selection due to the existence of either outliers[...]

BackgroundDeletions and amplifications of the human genomic DNA copy number are the causes ofnumerous diseases, such as, various forms of cancer. Therefore, the detection[...]

Robust high-dimensional data analysis has become an important and challenging task in complex Big Data analysis due to the high-dimensionality and data contamination. One[...]

Bivariate interval censored data arises in many applications. However, both theoreticaland computational investigations for this type of data are limited because of theco[...]

High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commo[...]