Articles with public access mandates - Runze LiLearn more
Not available anywhere: 4
Threshold selection in feature screening for error rate control
X Guo, H Ren, C Zou, R Li
Journal of the American Statistical Association 118 (543), 1773-1785, 2023
Mandates: US National Science Foundation, National Natural Science Foundation of China
Iterative conditional maximization algorithm for nonconcave penalized likelihood
Y Zhang, R Li
Nonparametric Statistics and Mixture Models: A Festschrift in Honor of …, 2011
Mandates: US National Institutes of Health
Feature-splitting algorithms for ultrahigh dimensional quantile regression
J Wen, S Yang, CD Wang, Y Jiang, R Li
Journal of Econometrics, 105426, 2023
Mandates: US National Science Foundation, US National Institutes of Health, National …
Testing the effects of high-dimensional covariates via aggregating cumulative covariances
R Li, K Xu, Y Zhou, L Zhu
Journal of the American Statistical Association 118 (543), 2184-2194, 2023
Mandates: US National Science Foundation, National Natural Science Foundation of China
Available somewhere: 172
One-step sparse estimates in nonconcave penalized likelihood models
H Zou, R Li
Annals of Statistics 36 (4), 1509, 2008
Mandates: US National Institutes of Health
Sensitivity and specificity of information criteria
JJ Dziak, DL Coffman, ST Lanza, R Li, LS Jermiin
Briefings in bioinformatics 21 (2), 553-565, 2020
Mandates: US National Institutes of Health
Feature screening via distance correlation learning
R Li, W Zhong, L Zhu
Journal of the American Statistical Association 107 (499), 1129-1139, 2012
Mandates: US National Institutes of Health
Model-free feature screening for ultrahigh-dimensional data
LP Zhu, L Li, R Li, LX Zhu
Journal of the American Statistical Association 106 (496), 1464-1475, 2011
Mandates: US National Institutes of Health
Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs.
LM Collins, JJ Dziak, R Li
Psychological methods 14 (3), 202, 2009
Mandates: US National Institutes of Health
Regularization parameter selections via generalized information criterion
Y Zhang, R Li, CL Tsai
Journal of the American statistical Association 105 (489), 312-323, 2010
Mandates: US National Institutes of Health
New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
B Kai, R Li, H Zou
Annals of statistics 39 (1), 305, 2011
Mandates: US National Institutes of Health
Quantile regression for analyzing heterogeneity in ultra-high dimension
L Wang, Y Wu, R Li
Journal of the American Statistical Association 107 (497), 214-222, 2012
Mandates: US National Institutes of Health
A time-varying effect model for intensive longitudinal data.
X Tan, MP Shiyko, R Li, Y Li, L Dierker
Psychological methods 17 (1), 61, 2012
Mandates: US National Institutes of Health
The Bayesian lasso for genome-wide association studies
J Li, K Das, G Fu, R Li, R Wu
Bioinformatics 27 (4), 516-523, 2011
Mandates: US National Institutes of Health
Estimation and testing for partially linear single-index models
H Liang, X Liu, R Li, CL Tsai
Annals of statistics 38 (6), 3811, 2010
Mandates: US National Institutes of Health
Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression
B Kai, R Li, H Zou
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2010
Mandates: US National Institutes of Health
Model-free feature screening for ultrahigh dimensional discriminant analysis
H Cui, R Li, W Zhong
Journal of the American Statistical Association 110 (510), 630-641, 2015
Mandates: US National Institutes of Health, National Natural Science Foundation of China
Feature selection for varying coefficient models with ultrahigh-dimensional covariates
J Liu, R Li, R Wu
Journal of the American Statistical Association 109 (505), 266-274, 2014
Mandates: US National Institutes of Health
Variable selection for partially linear models with measurement errors
H Liang, R Li
Journal of the American Statistical Association 104 (485), 234-248, 2009
Mandates: US National Institutes of Health
Calibrating non-convex penalized regression in ultra-high dimension
L Wang, Y Kim, R Li
Annals of statistics 41 (5), 2505, 2013
Mandates: US National Institutes of Health
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