Xiaoli Gao

Dr. Xiaoli Gao received her Ph.D. in Statistics from the University of Iowa in 2008 and joined UNCG in 2013. Her research interests include High-dimensional Data analysis, Shrinkage analysis, Statistical Genetics, Change point and Survival Analysis. More recent papers can be found on her personal webpage.

There are 8 included publications by Xiaoli Gao :

TitleDateViewsBrief Description
Asymptotic analysis of high-dimensional LAD regression with Lasso 2010 289 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 properties of the Lasso in the context of least squares regression. However, the lea...
A flexible shrinkage operator for fussy grouped variable selection 2016 69 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, we propose a family of shrinkage variable selection operators by controlling t...
A note on the generalized degrees of freedom under the L1 loss function 2011 75 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 manuscript, we consider two definitions of generalized degrees of freedom for a modelin...
Penalized weighted low-rank approximation for robust recovery of recurrent copy number variations 2015 346 2015-2016 UNCG University Libraries Open Access Publishing Fund Grant Winner. BackgroundCopy number variation (CNV) analysis has become one of the most important researchareas for understanding complex disease. With increasing resolution of array-ba...
Post selection shrinkage estimation for high-dimensional data analysis 2017 18 In high-dimensional data settings where p ยป n, many penalized regularization approaches were studied for simultaneous variable selection and estimation. However, with the existence of covariates with weak effect, many existing variable selection meth...
Rejoinder: Post Selection Shrinkage Estimation for High Dimensional Data Analysis 2017 2 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 absolute shrinkage and selection operator (LASSO) may be very effective in detecting ...
A robust penalized method for the analysis of noisy DNA copy number data 2010 414 BackgroundDeletions and amplifications of the human genomic DNA copy number are the causes ofnumerous diseases, such as, various forms of cancer. Therefore, the detection of DNAcopy number variations (CNV) is important in understanding the genetic ba...
Sieve Estimation with Bivariate Interval Censored Data 2011 374 Bivariate interval censored data arises in many applications. However, both theoreticaland computational investigations for this type of data are limited because of thecomplicated structure of bivariate censoring. In this paper, we propose a two-stag...