193 citations · 233 across the 2 of their papers we have counts for
3 papers
stat.AP2011★ 193 cited
Random lasso
Sijian Wang, Bin Nan, Saharon Rosset +1
We propose a computationally intensive method, the random lasso method, for variable selection in linear models. The method consists of two major steps. In step 1, the lasso method…
math.ST2009★ 40 cited
Asymptotic theory for the semiparametric accelerated failure time model with missing data
Bin Nan, John D. Kalbfleisch, Menggang Yu
We consider a class of doubly weighted rank-based estimating methods for the transformation (or accelerated failure time) model with missing data as arise, for example, in case-coh…
math.ST2004
Information bounds for Cox regression models with missing data
Bin Nan, Mary J. Emond, Jon A. Wellner
We derive information bounds for the regression parameters in Cox models when data are missing at random. These calculations are of interest for understanding the behavior of effic…