Yang Feng
Yang Feng
Associate Professor of Biostatistics, School of Global Public Health, New York University
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα nyu.edu - Αρχική σελίδα
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The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.
L Shi, G Campbell, WD Jones, F Campagne, Z Wen, SJ Walker, Z Su, ...
Nature biotechnology 28 (8), 827, 2010
7052010
Nonparametric independence screening in sparse ultra-high-dimensional additive models
J Fan, Y Feng, R Song
Journal of the American Statistical Association 106, 544-557, 2011
4662011
Network exploration via the adaptive LASSO and SCAD penalties
J Fan, Y Feng, Y Wu
The annals of applied statistics 3 (2), 521, 2009
3692009
A road to classification in high dimensional space: the regularized optimal affine discriminant
J Fan, Y Feng, X Tong
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2012
1702012
High-dimensional variable selection for Cox’s proportional hazards model
J Fan, Y Feng, Y Wu
Borrowing Strength: Theory Powering Applications–A Festschrift for Lawrence …, 2010
1492010
How many communities are there?
DF Saldana, Y Yu, Y Feng
Journal of Computational and Graphical Statistics 26 (1), 171-181, 2017
782017
SIS: An R Package for Sure Independence Screening in Ultrahigh Dimensional Statistical Models
DF Saldana, Y Feng
Journal of Statistical Software 83 (2), 1-25, 2018
77*2018
Model selection for high-dimensional quadratic regression via regularization
N Hao, Y Feng, HH Zhang
Journal of the American Statistical Association 113 (522), 615-625, 2018
532018
Feature Augmentation via Nonparametrics and Selection (FANS) in high-dimensional classification
J Fan, Y Feng, J Jiang, X Tong
Journal of the American Statistical Association 111 (513), 275-287, 2016
342016
Variable selection and prediction with incomplete high-dimensional data
Y Liu, Y Wang, Y Feng, MM Wall
The annals of applied statistics 10 (1), 418, 2016
332016
Modified cross-validation for penalized high-dimensional linear regression models
Y Yu, Y Feng
Journal of Computational and Graphical Statistics 23 (4), 1009-1027, 2014
322014
Local quasi-likelihood with a parametric guide
J Fan, Y Wu, Y Feng
Annals of statistics 37 (6B), 4153, 2009
322009
Neyman-Pearson classification algorithms and NP receiver operating characteristics
X Tong, Y Feng, JJ Li
Science Advances 4 (2), eaao1659, 2018
302018
Post selection shrinkage estimation for high‐dimensional data analysis
X Gao, SE Ahmed, Y Feng
Applied Stochastic Models in Business and Industry 33 (2), 97-120, 2017
292017
The restricted consistency property of leave--out cross-validation for high-dimensional variable selection
Y Feng, Y Yu
Statistica Sinica 29, 1607-1630, 2019
28*2019
A survey on Neyman‐Pearson classification and suggestions for future research
X Tong, Y Feng, A Zhao
Wiley Interdisciplinary Reviews: Computational Statistics 8 (2), 64-81, 2016
282016
A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models
J Fan, Y Feng, L Xia
Journal of Econometrics, 2020
24*2020
Tuning-parameter selection in regularized estimations of large covariance matrices
Y Fang, B Wang, Y Feng
Journal of Statistical Computation and Simulation 86 (3), 494-509, 2016
182016
JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data
J Ji, D He, Y Feng, Y He, F Xue, L Xie
Bioinformatics 33 (19), 3080-3087, 2017
162017
Neyman-Pearson classification under high-dimensional settings
A Zhao, Y Feng, L Wang, X Tong
The Journal of Machine Learning Research 17 (1), 7469-7507, 2016
162016
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