Artykuły udostępnione publicznie: - Lan WangWięcej informacji
Dostępne w jakimś miejscu: 39
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
Upoważnienia: US National Institutes of Health
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
X He, L Wang, HG Hong
Upoważnienia: 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
Upoważnienia: US National Institutes of Health
A high-dimensional nonparametric multivariate test for mean vector
L Wang, B Peng, R Li
Journal of the American Statistical Association 110 (512), 1658-1669, 2015
Upoważnienia: US National Institutes of Health
Partially linear additive quantile regression in ultra-high dimension
B Sherwood, L Wang
Upoważnienia: US National Science Foundation
Quantile-optimal treatment regimes
L Wang, Y Zhou, R Song, B Sherwood
Journal of the American Statistical Association 113 (523), 1243-1254, 2018
Upoważnienia: US National Science Foundation, US National Institutes of Health
Variable selection for support vector machines in moderately high dimensions
X Zhang, Y Wu, L Wang, R Li
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2016
Upoważnienia: US National Science Foundation, US National Institutes of Health
Weighted Wilcoxon-type smoothly clipped absolute deviation method
L Wang, R Li
Biometrics 65 (2), 564-571, 2009
Upoważnienia: US National Institutes of Health
Local rank inference for varying coefficient models
L Wang, B Kai, R Li
Journal of the American Statistical Association 104 (488), 1631-1645, 2009
Upoważnienia: US National Institutes of Health
A tuning-free robust and efficient approach to high-dimensional regression
L Wang, B Peng, J Bradic, R Li, Y Wu
Journal of the American Statistical Association 115 (532), 1700-1714, 2020
Upoważnienia: US National Science Foundation, US National Institutes of Health
Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering
J Chang, W Zhou, WX Zhou, L Wang
Biometrics 73 (1), 31-41, 2017
Upoważnienia: US National Science Foundation, Australian Research Council, National …
High-dimensional quantile regression: Convolution smoothing and concave regularization
KM Tan, L Wang, WX Zhou
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022
Upoważnienia: US National Science Foundation, US National Institutes of Health
A parallel algorithm for large-scale nonconvex penalized quantile regression
L Yu, N Lin, L Wang
Journal of Computational and Graphical Statistics 26 (4), 935-939, 2017
Upoważnienia: US National Science Foundation
An error bound for l1-norm support vector machine coefficients in ultra-high dimension
B Peng, L Wang, Y Wu
Journal of Machine Learning Research 17 (233), 1-26, 2016
Upoważnienia: US National Science Foundation, US National Institutes of Health
A survey of tuning parameter selection for high-dimensional regression
Y Wu, L Wang
Annual review of statistics and its application 7 (1), 209-226, 2020
Upoważnienia: US National Science Foundation
Using quantile regression to create baseline norms for neuropsychological tests
B Sherwood, AXH Zhou, S Weintraub, L Wang
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 2, 12-18, 2016
Upoważnienia: US National Science Foundation, US National Institutes of Health
Fairness-oriented learning for optimal individualized treatment rules
EX Fang, Z Wang, L Wang
Journal of the American Statistical Association 118 (543), 1733-1746, 2023
Upoważnienia: US National Science Foundation
PGEE: an R package for analysis of longitudinal data with high-dimensional covariates
G Inan, L Wang
Upoważnienia: US National Science Foundation
Quantile regression for recurrent gap time data
X Luo, CY Huang, L Wang
Biometrics 69 (2), 375-385, 2013
Upoważnienia: US National Institutes of Health
An alternative robust estimator of average treatment effect in causal inference
J Liu, Y Ma, L Wang
Biometrics 74 (3), 910-923, 2018
Upoważnienia: US National Science Foundation, US National Institutes of Health
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