publications

2024

1. L1-Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes

   High-dimensional Statistics, Classification

   Ye Tian, Henry Rusinek, Arjun V Masurkar, and Yang Feng

   Statistics in Medicine, 2024

   DOI arXiv Bib

   @article{tian2024l1,
     title = {L1-Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes},
     author = {Tian, Ye and Rusinek, Henry and Masurkar, Arjun V and Feng, Yang},
     journal = {Statistics in Medicine},
     year = {2024},
     publisher = {John Wiley \& Sons, Inc.},
     doi = {10.1002/sim.10263}
   }

2. Neyman-pearson multi-class classification via cost-sensitive learning

   Machine Learning, Neyman-Pearson Classification

   Ye Tian, and Yang Feng

   Journal of the American Statistical Association, 2024

   DOI Bib

   @article{tian2024neyman,
     author = {Tian, Ye and Feng, Yang},
     journal = {Journal of the American Statistical Association},
     title = {Neyman-pearson multi-class classification via cost-sensitive learning},
     year = {2024},
     number = {just-accepted},
     pages = {1--23},
     publisher = {Taylor \& Francis},
     arixv = {2111.04597},
     doi = {10.1080/01621459.2024.2402567},
   }

3. Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models

   Machine Learning, Transfer Learning, Multi-task Learning

   Ye Tian, Haolei Weng, Lucy Xia, and Yang Feng

   arXiv preprint arXiv:2209.15224, 2024

   arXiv Bib

   @article{tian2024unsupervised,
     author = {Tian, Ye and Weng, Haolei and Xia, Lucy and Feng, Yang},
     journal = {arXiv preprint arXiv:2209.15224},
     title = {Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models},
     year = {2024},
   }

4. Prognostic value of DNA methylation subclassification, aneuploidy, and CDKN2A/B homozygous deletion in predicting clinical outcome of IDH mutant astrocytomas

   Kristyn Galbraith, Mekka Garcia, Siyu Wei, Anna Chen, Chanel Schroff, and 6 more authors

   Neuro-oncology, 2024

   DOI Bib

   @article{galbraith2024prognostic,
     author = {Galbraith, Kristyn and Garcia, Mekka and Wei, Siyu and Chen, Anna and Schroff, Chanel and Serrano, Jonathan and Pacione, Donato and Placantonakis, Dimitris G and William, Christopher M and Faustin, Arline and others},
     journal = {Neuro-oncology},
     number = {6},
     pages = {1042--1051},
     publisher = {Oxford University Press US},
     title = {Prognostic value of DNA methylation subclassification, aneuploidy, and CDKN2A/B homozygous deletion in predicting clinical outcome of IDH mutant astrocytomas},
     volume = {26},
     doi = {10.1093/neuonc/noae009},
     year = {2024}
   }

5. Omics feature selection with the extended SIS R package: identification of a body mass index epigenetic multi-marker in the Strong Heart Study

   Machine Learning, High-dimensional Statistics, Epidemiology

   Arce Domingo-Relloso, Yang Feng, Zulema Rodriguez-Hernandez, Karin Haack, Shelley A Cole, and 3 more authors

   American Journal of Epidemiology, 2024

   DOI Bib

   @article{domingo2024omics,
     author = {Domingo-Relloso, Arce and Feng, Yang and Rodriguez-Hernandez, Zulema and Haack, Karin and Cole, Shelley A and Navas-Acien, Ana and Tellez-Plaza, Maria and Bermudez, Jose D},
     journal = {American Journal of Epidemiology},
     pages = {kwae006},
     publisher = {Oxford University Press},
     title = {Omics feature selection with the extended SIS R package: identification of a body mass index epigenetic multi-marker in the Strong Heart Study},
     doi = {10.1093/aje/kwae006},
     year = {2024},
   }

6. Machine collaboration

   Machine Learning, High-dimensional Statistics

   Qingfeng Liu, and Yang Feng

   Stat, 2024

   arXiv Bib

   @article{liu2024machine,
     author = {Liu, Qingfeng and Feng, Yang},
     journal = {Stat},
     number = {1},
     pages = {e661},
     publisher = {Wiley Online Library},
     title = {Machine collaboration},
     volume = {13},
     year = {2024},
   }

7. Federated Transfer Learning with Differential Privacy

   Machine Learning, Transfer Learning, Differential Privacy, Federated Learning

   Mengchu Li, Ye Tian, Yang Feng, and Yi Yu

   arXiv preprint arXiv:2403.11343, 2024

   arXiv Bib

   @article{li2024federated,
     author = {Li, Mengchu and Tian, Ye and Feng, Yang and Yu, Yi},
     journal = {arXiv preprint arXiv:2403.11343},
     title = {Federated Transfer Learning with Differential Privacy},
     year = {2024},
   }

8. Yang Feng and Jiajin Sun’s contribution to the Discussion of ‘Root and community inference on the latent growth process of a network’by Crane and Xu

   Network Analysis

   Yang Feng, and Jiajin Sun

   Journal of the Royal Statistical Society Series B: Statistical Methodology, 2024

   DOI Bib

   @article{feng2024yang,
     author = {Feng, Yang and Sun, Jiajin},
     journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
     number = {4},
     pages = {875--878},
     publisher = {Oxford University Press US},
     title = {Yang Feng and Jiajin Sun's contribution to the Discussion of `Root and community inference on the latent growth process of a network'by Crane and Xu},
     volume = {86},
     doi = {10.1093/jrsssb/qkae055},
     year = {2024},
   }

9. Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms

   Machine Learning, High-dimensional Statistics, Federated Learning, Transfer Learning, Multi-task Learning

   Ye Tian, Haolei Weng, and Yang Feng

   In Forty-first International Conference on Machine Learning, 2024

   arXiv Bib

   @inproceedings{tian2024towards,
     author = {Tian, Ye and Weng, Haolei and Feng, Yang},
     booktitle = {Forty-first International Conference on Machine Learning},
     title = {Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms},
     year = {2024},
   }

10. Variational Nonparametric Inference in Functional Stochastic Block Model

    Network Analysis

    Zuofeng Shang, Peijun Sang, Yang Feng, and Chong Jin

    arXiv preprint arXiv:2407.00564, 2024

    arXiv Bib

    @article{shang2024variational,
      author = {Shang, Zuofeng and Sang, Peijun and Feng, Yang and Jin, Chong},
      journal = {arXiv preprint arXiv:2407.00564},
      title = {Variational Nonparametric Inference in Functional Stochastic Block Model},
      year = {2024},
    }

11. Racial distribution of molecularly classified brain tumors

    Camila S Fang, Wanyi Wang, Chanel Schroff, Misha Movahed-Ezazi, Varshini Vasudevaraja, and 6 more authors

    Neuro-Oncology Advances, 2024

    DOI Bib

    @article{fang2024racial,
      author = {Fang, Camila S and Wang, Wanyi and Schroff, Chanel and Movahed-Ezazi, Misha and Vasudevaraja, Varshini and Serrano, Jonathan and Sulman, Erik P and Golfinos, John G and Orringer, Daniel and Galbraith, Kristyn and others},
      journal = {Neuro-Oncology Advances},
      number = {1},
      pages = {vdae135},
      publisher = {Oxford University Press US},
      title = {Racial distribution of molecularly classified brain tumors},
      volume = {6},
      doi = {10.1093/noajnl/vdae135},
      year = {2024}
    }

12. Multi-label Random Subspace Ensemble Classification1

    Machine Learning, High-dimensional Statistics

    Fan Bi, Jianan Zhu, and Yang Feng

    Journal of Computational and Graphical Statistics, 2024

    DOI Bib

    @article{bi2024multi,
      author = {Bi, Fan and Zhu, Jianan and Feng, Yang},
      journal = {Journal of Computational and Graphical Statistics},
      number = {just-accepted},
      pages = {1--20},
      publisher = {Taylor \& Francis},
      title = {Multi-label Random Subspace Ensemble Classification1},
      doi = {10.1080/10618600.2024.2421248},
      year = {2024},
    }

2023

1. Spectral clustering via adaptive layer aggregation for multi-layer networks

   Network Analysis

   Sihan Huang, Haolei Weng, and Yang Feng

   Journal of Computational and Graphical Statistics, 2023

   Bib PDF

   @article{huang2023spectral,
     author = {Huang, Sihan and Weng, Haolei and Feng, Yang},
     journal = {Journal of Computational and Graphical Statistics},
     number = {32},
     pages = {1170--1184},
     publisher = {Taylor \& Francis},
     title = {Spectral clustering via adaptive layer aggregation for multi-layer networks},
     year = {2023},
   }

2. Transfer learning under high-dimensional generalized linear models

   Transfer Learning, High-dimensional Statistics

   Ye Tian, and Yang Feng

   Journal of the American Statistical Association, 2023

   Bib PDF

   @article{tian2023transfer,
     author = {Tian, Ye and Feng, Yang},
     journal = {Journal of the American Statistical Association},
     title = {Transfer learning under high-dimensional generalized linear models},
     year = {2023},
     number = {544},
     pages = {2684--2697},
     volume = {118},
     publisher = {Taylor \& Francis},
   }

3. Variable selection for high-dimensional generalized linear model with block-missing data

   High-dimensional Statistics

   Yifan He, Yang Feng, and Xinyuan Song

   Scandinavian Journal of Statistics, 2023

   DOI Bib

   @article{he2023variable,
     author = {He, Yifan and Feng, Yang and Song, Xinyuan},
     journal = {Scandinavian Journal of Statistics},
     number = {3},
     pages = {1279--1297},
     title = {Variable selection for high-dimensional generalized linear model with block-missing data},
     volume = {50},
     doi = {10.1111/sjos.12632},
     year = {2023},
   }

4. Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

   Machine Learning, Transfer Learning, Multi-task Learning

   Ye Tian, Yuqi Gu, and Yang Feng

   arXiv preprint arXiv:2303.17765, 2023

   arXiv Bib

   @article{tian2023learning,
     author = {Tian, Ye and Gu, Yuqi and Feng, Yang},
     journal = {arXiv preprint arXiv:2303.17765},
     title = {Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness},
     year = {2023},
   }

5. DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation

   High-dimensional Statistics

   Yifan He, Ruiyang Wu, Yong Zhou, and Yang Feng

   Journal of the American Statistical Association, 2023

   Bib PDF

   @article{he2023ddac,
     author = {He, Yifan and Wu, Ruiyang and Zhou, Yong and Feng, Yang},
     journal = {Journal of the American Statistical Association},
     title = {DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation},
     year = {2023},
     pages = {1--12},
     publisher = {Taylor \& Francis},
   }

6. PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection

   Network Analysis

   Sihan Huang, Jiajin Sun, and Yang Feng

   Journal of the American Statistical Association, 2023

   Bib PDF

   @article{huang2023pcabm,
     author = {Huang, Sihan and Sun, Jiajin and Feng, Yang},
     journal = {Journal of the American Statistical Association},
     title = {PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection},
     year = {2023},
     pages = {1--13},
     publisher = {Taylor \& Francis},
   }

7. Semiparametric Modeling and Analysis for Longitudinal Network Data

   Network Analysis, Nonparametric Statistics

   Yinqiu He, Jiajin Sun, Yuang Tian, Zhiliang Ying, and Yang Feng

   arXiv preprint arXiv:2308.12227, 2023

   arXiv Bib

   @article{he2023semiparametric,
     author = {He, Yinqiu and Sun, Jiajin and Tian, Yuang and Ying, Zhiliang and Feng, Yang},
     journal = {arXiv preprint arXiv:2308.12227},
     title = {Semiparametric Modeling and Analysis for Longitudinal Network Data},
     year = {2023},
   }

8. A flexible quasi-likelihood model for microbiome abundance count data

   Yiming Shi, Huilin Li, Chan Wang, Jun Chen, Hongmei Jiang, and 5 more authors

   Statistics in Medicine, 2023

   DOI Bib

   @article{shi2023flexible,
     author = {Shi, Yiming and Li, Huilin and Wang, Chan and Chen, Jun and Jiang, Hongmei and Shih, Ya-Chen T and Zhang, Haixiang and Song, Yizhe and Feng, Yang and Liu, Lei},
     journal = {Statistics in Medicine},
     number = {25},
     pages = {4632--4643},
     publisher = {John Wiley \& Sons, Inc. Hoboken, USA},
     title = {A flexible quasi-likelihood model for microbiome abundance count data},
     volume = {42},
     doi = {10.1002/sim.9880},
     year = {2023}
   }

9. Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment

   Machine Learning, Causal Inference

   Siyu Heng, Jiawei Zhang, and Yang Feng

   arXiv preprint arXiv:2310.18556, 2023

   arXiv Bib

   @article{heng2023design,
     author = {Heng, Siyu and Zhang, Jiawei and Feng, Yang},
     journal = {arXiv preprint arXiv:2310.18556},
     title = {Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment},
     year = {2023},
   }

10. Simulation of New York City’s Ventilator Allocation Guideline During the Spring 2020 COVID-19 Surge

    Epidemiology

    B Corbett Walsh, Jianan Zhu, Yang Feng, Kenneth A Berkowitz, Rebecca A Betensky, and 2 more authors

    JAMA network open, 2023

    DOI Bib

    @article{walsh2023simulation,
      author = {Walsh, B Corbett and Zhu, Jianan and Feng, Yang and Berkowitz, Kenneth A and Betensky, Rebecca A and Nunnally, Mark E and Pradhan, Deepak R},
      journal = {JAMA network open},
      number = {10},
      pages = {e2336736--e2336736},
      publisher = {American Medical Association},
      title = {Simulation of New York City's Ventilator Allocation Guideline During the Spring 2020 COVID-19 Surge},
      volume = {6},
      doi = {10.1001/jamanetworkopen.2023.36736},
      year = {2023},
    }

11. Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models

    High-dimensional Statistics

    Ye Tian, and Yang Feng

    Test, 2023

    Bib PDF

    @article{tian2023comments,
      author = {Tian, Ye and Feng, Yang},
      journal = {Test},
      number = {4},
      pages = {1172--1176},
      publisher = {Springer Berlin Heidelberg Berlin/Heidelberg},
      title = {Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models},
      volume = {32},
      year = {2023},
    }

2022

1. A likelihood-ratio type test for stochastic block models with bounded degrees

   Network Analysis

   Mingao Yuan, Yang Feng, and Zuofeng Shang

   Journal of Statistical Planning and Inference, 2022

   Bib PDF

   @article{yuan2022likelihood,
     author = {Yuan, Mingao and Feng, Yang and Shang, Zuofeng},
     journal = {Journal of Statistical Planning and Inference},
     pages = {98--119},
     publisher = {North-Holland},
     title = {A likelihood-ratio type test for stochastic block models with bounded degrees},
     volume = {219},
     year = {2022},
   }

2. Targeting predictors via partial distance correlation with applications to financial forecasting

   High-dimensional Statistics

   Kashif Yousuf, and Yang Feng

   Journal of Business & Economic Statistics, 2022

   Bib PDF

   @article{yousuf2022targeting,
     author = {Yousuf, Kashif and Feng, Yang},
     journal = {Journal of Business \& Economic Statistics},
     number = {3},
     pages = {1007--1019},
     publisher = {Taylor \& Francis},
     title = {Targeting predictors via partial distance correlation with applications to financial forecasting},
     volume = {40},
     year = {2022},
   }

3. Community detection with nodal information: likelihood and its variational approximation

   Network Analysis

   Haolei Weng, and Yang Feng

   Stat, 2022

   Bib PDF

   @article{weng2022community,
     author = {Weng, Haolei and Feng, Yang},
     journal = {Stat},
     pages = {e428},
     title = {Community detection with nodal information: likelihood and its variational approximation},
     year = {2022},
   }

4. Testing community structure for hypergraphs

   Network Analysis

   Mingao Yuan, Ruiqi Liu, Yang Feng, and Zuofeng Shang

   The Annals of Statistics, 2022

   Bib PDF

   @article{yuan2022testing,
     author = {Yuan, Mingao and Liu, Ruiqi and Feng, Yang and Shang, Zuofeng},
     journal = {The Annals of Statistics},
     title = {Testing community structure for hypergraphs},
     year = {2022},
     number = {1},
     pages = {147--169},
     volume = {50},
     publisher = {Institute of Mathematical Statistics},
   }

5. Large-scale model selection in misspecified generalized linear models

   High-dimensional Statistics

   Emre Demirkaya, Yang Feng, Pallavi Basu, and Jinchi Lv

   Biometrika, 2022

   Bib PDF

   @article{demirkaya2022large,
     author = {Demirkaya, Emre and Feng, Yang and Basu, Pallavi and Lv, Jinchi},
     journal = {Biometrika},
     title = {Large-scale model selection in misspecified generalized linear models},
     year = {2022},
     number = {1},
     pages = {123--136},
     volume = {109},
     publisher = {Oxford University Press},
   }

6. Discussion of “Cocitation and Coauthorship Networks of Statisticians”

   Network Analysis

   Haolei Weng, and Yang Feng

   Journal of Business & Economic Statistics, 2022

   Bib PDF

   @article{weng2022discussion,
     author = {Weng, Haolei and Feng, Yang},
     journal = {Journal of Business \& Economic Statistics},
     number = {2},
     pages = {486--490},
     publisher = {Taylor \& Francis},
     title = {Discussion of ``Cocitation and Coauthorship Networks of Statisticians''},
     volume = {40},
     year = {2022},
   }

7. Association of hyperglycemia and molecular subclass on survival in IDH-wildtype glioblastoma

   Elisa K Liu, Varshini Vasudevaraja, Vladislav O Sviderskiy, Yang Feng, Ivy Tran, and 6 more authors

   Neuro-Oncology Advances, 2022

   DOI Bib

   @article{liu2022association,
     author = {Liu, Elisa K and Vasudevaraja, Varshini and Sviderskiy, Vladislav O and Feng, Yang and Tran, Ivy and Serrano, Jonathan and Cordova, Christine and Kurz, Sylvia C and Golfinos, John G and Sulman, Erik P and others},
     journal = {Neuro-Oncology Advances},
     number = {1},
     pages = {vdac163},
     publisher = {Oxford University Press US},
     title = {Association of hyperglycemia and molecular subclass on survival in IDH-wildtype glioblastoma},
     volume = {4},
     doi = {10.1093/noajnl/vdac163},
     year = {2022}
   }

8. Clinical, Pathological, and Molecular Characteristics of Diffuse Spinal Cord Gliomas

   Mekka R Garcia, Yang Feng, Varshini Vasudevaraja, Kristyn Galbraith, Jonathan Serrano, and 6 more authors

   Journal of Neuropathology & Experimental Neurology, 2022

   DOI Bib

   @article{garcia2022clinical,
     author = {Garcia, Mekka R and Feng, Yang and Vasudevaraja, Varshini and Galbraith, Kristyn and Serrano, Jonathan and Thomas, Cheddhi and Radmanesh, Alireza and Hidalgo, Eveline T and Harter, David H and Allen, Jeffrey C and others},
     journal = {Journal of Neuropathology \& Experimental Neurology},
     number = {11},
     pages = {865--872},
     publisher = {Oxford Academic},
     title = {Clinical, Pathological, and Molecular Characteristics of Diffuse Spinal Cord Gliomas},
     volume = {81},
     doi = {10.1093/jnen/nlac075},
     year = {2022}
   }

9. Differential Role of Hyperglycemia on Survival in IDH-wildtype Glioblastoma Subclasses

   Elisa Liu, Varshini Vasudevaraja, Vladislav Sviderskiy, Yang Feng, Ivy Tran, and 6 more authors

   In JOURNAL OF NEUROPATHOLOGY AND EXPERIMENTAL NEUROLOGY, 2022

   DOI Bib

   @inproceedings{liu2022differential,
     author = {Liu, Elisa and Vasudevaraja, Varshini and Sviderskiy, Vladislav and Feng, Yang and Tran, Ivy and Serrano, Jonathan and Cordova, Christine and Kurz, Sylvia and Golfinos, John and Sulman, Erik and others},
     booktitle = {JOURNAL OF NEUROPATHOLOGY AND EXPERIMENTAL NEUROLOGY},
     number = {6},
     organization = {OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA},
     pages = {440--440},
     title = {Differential Role of Hyperglycemia on Survival in IDH-wildtype Glioblastoma Subclasses},
     volume = {81},
     doi = {10.1093/noajnl/vdac163},
     year = {2022}
   }

2021

1. RaSE: Random Subspace Ensemble Classification

   High-dimensional Statistics, Classification, Machine Learning

   Ye Tian, and Yang Feng

   Journal of Machine Learning Research, 2021

   Bib PDF

   @article{tian2021raseclassification,
     author = {Tian, Ye and Feng, Yang},
     journal = {Journal of Machine Learning Research},
     title = {RaSE: Random Subspace Ensemble Classification},
     year = {2021},
     date-modified = {2024-11-12 13:09:11 -0500},
     read = {0},
   }

2. Visceral adipose tissue in patients with COVID-19: risk stratification for severity

   Hersh Chandarana, Bari Dane, Artem Mikheev, Myles T Taffel, Yang Feng, and 1 more author

   Abdominal Radiology, 2021

   Bib PDF

   @article{chandarana2021visceral,
     author = {Chandarana, Hersh and Dane, Bari and Mikheev, Artem and Taffel, Myles T and Feng, Yang and Rusinek, Henry},
     journal = {Abdominal Radiology},
     number = {2},
     pages = {818--825},
     publisher = {Springer US},
     title = {Visceral adipose tissue in patients with COVID-19: risk stratification for severity},
     volume = {46},
     year = {2021}
   }

3. Model Averaging for Nonlinear Regression Models

   Model Averaging

   Y Feng, Q Liu, Q Yao, and G Zhao

   Journal of Business & Economic Statistics, 2021

   Bib PDF

   @article{feng2021model,
     author = {Feng, Y and Liu, Q and Yao, Q and Zhao, G},
     journal = {Journal of Business \& Economic Statistics},
     title = {Model Averaging for Nonlinear Regression Models},
     year = {2021},
   }

4. The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of US COVID-19 Cases

   Epidemiology, Machine Learning

   Francesca Tang, Yang Feng, Hamza Chiheb, and Jianqing Fan

   Journal of the American Statistical Association, 2021

   Bib PDF

   @article{tang2021interplay,
     author = {Tang, Francesca and Feng, Yang and Chiheb, Hamza and Fan, Jianqing},
     journal = {Journal of the American Statistical Association},
     title = {The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of US COVID-19 Cases},
     year = {2021},
   }

5. RaSE: A Variable Screening Framework via Random Subspace Ensembles

   Machine Learning, High-dimensional Statistics

   Ye Tian, and Yang Feng

   Journal of American Statistical Association, 2021

   Bib PDF

   @article{tian2021rasescreening,
     author = {Tian, Ye and Feng, Yang},
     journal = {Journal of American Statistical Association},
     title = {RaSE: A Variable Screening Framework via Random Subspace Ensembles},
     year = {2021},
     date-modified = {2024-11-12 13:08:50 -0500},
   }

6. Mediation effect selection in high-dimensional and compositional microbiome data

   High-dimensional Statistics

   Haixiang Zhang, Jun Chen, Yang Feng, Chan Wang, Huilin Li, and 1 more author

   Statistics in medicine, 2021

   DOI Bib

   @article{zhang2021mediation,
     author = {Zhang, Haixiang and Chen, Jun and Feng, Yang and Wang, Chan and Li, Huilin and Liu, Lei},
     journal = {Statistics in medicine},
     number = {4},
     pages = {885--896},
     publisher = {Wiley Online Library},
     title = {Mediation effect selection in high-dimensional and compositional microbiome data},
     volume = {40},
     year = {2021},
     doi = {10.1002/sim.8808},
   }

7. Imbalanced classification: A paradigm-based review

   Classification

   Yang Feng, Min Zhou, and Xin Tong

   Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021

   Bib PDF

   @article{feng2021imbalanced,
     author = {Feng, Yang and Zhou, Min and Tong, Xin},
     journal = {Statistical Analysis and Data Mining: The ASA Data Science Journal},
     number = {5},
     pages = {383--406},
     publisher = {Wiley Subscription Services, Inc., A Wiley Company Hoboken},
     title = {Imbalanced classification: A paradigm-based review},
     volume = {14},
     year = {2021},
   }

8. Comparison of solid tissue sequencing and liquid biopsy accuracy in identification of clinically relevant gene mutations and rearrangements in lung adenocarcinomas

   Lawrence Hsu Lin, Douglas HR Allison, Yang Feng, George Jour, Kyung Park, and 6 more authors

   Modern Pathology, 2021

   Bib PDF

   @article{lin2021comparison,
     author = {Lin, Lawrence Hsu and Allison, Douglas HR and Feng, Yang and Jour, George and Park, Kyung and Zhou, Fang and Moreira, Andre L and Shen, Guomiao and Feng, Xiaojun and Sabari, Joshua and others},
     journal = {Modern Pathology},
     number = {12},
     pages = {2168--2174},
     publisher = {Nature Publishing Group},
     title = {Comparison of solid tissue sequencing and liquid biopsy accuracy in identification of clinically relevant gene mutations and rearrangements in lung adenocarcinomas},
     volume = {34},
     year = {2021}
   }

9. Super RaSE: Super Random Subspace Ensemble Classification

   Machine Learning, High-dimensional Statistics, Classification

   Jianan Zhu, and Yang Feng

   Journal of Risk and Financial Management, 2021

   Bib PDF

   @article{zhu2021super,
     author = {Zhu, Jianan and Feng, Yang},
     journal = {Journal of Risk and Financial Management},
     number = {12},
     pages = {612},
     publisher = {Multidisciplinary Digital Publishing Institute},
     title = {Super RaSE: Super Random Subspace Ensemble Classification},
     volume = {14},
     year = {2021},
   }

10. Association of body composition parameters measured on CT with risk of hospitalization in patients with Covid-19

    Hersh Chandarana, Nisanard Pisuchpen, Rachel Krieger, Bari Dane, Artem Mikheev, and 3 more authors

    European Journal of Radiology, 2021

    DOI Bib

    @article{chandarana2021association,
      author = {Chandarana, Hersh and Pisuchpen, Nisanard and Krieger, Rachel and Dane, Bari and Mikheev, Artem and Feng, Yang and Kambadakone, Avinash and Rusinek, Henry},
      journal = {European Journal of Radiology},
      pages = {110031},
      publisher = {Elsevier},
      title = {Association of body composition parameters measured on CT with risk of hospitalization in patients with Covid-19},
      doi = {10.1016/j.ejrad.2021.110031},
      volume = {145},
      year = {2021}
    }

11. Targeted crisis risk control: A neyman-pearson approach

    Neyman-Pearson Classification

    Yang Feng, Xin Tong, and Weining Xin

    Available at SSRN, 2021

    DOI Bib

    @article{feng2021targeted,
      author = {Feng, Yang and Tong, Xin and Xin, Weining},
      journal = {Available at SSRN},
      title = {Targeted crisis risk control: A neyman-pearson approach},
      doi = {10.2139/ssrn.3945980},
      year = {2021},
    }

12. NCOG-11. ASSOCIATION OF HYPERGLYCEMIA AND TUMOR SUBCLASS ON SURVIVAL IN IDH-WILDTYPE GLIOBLASTOMA

    Elisa Liu, Varshini Vasudevaraja, Vladislav Sviderskiy, Yang Feng, Ivy Tran, and 6 more authors

    Neuro-Oncology, 2021

    DOI Bib

    @article{liu2021ncog,
      author = {Liu, Elisa and Vasudevaraja, Varshini and Sviderskiy, Vladislav and Feng, Yang and Tran, Ivy and Serrano, Jonathan and Cordova, Christine and Kurz, Sylvia and Golfinos, John and Sulman, Erik and others},
      journal = {Neuro-Oncology},
      number = {Suppl 6},
      pages = {vi154},
      publisher = {Oxford University Press},
      title = {NCOG-11. ASSOCIATION OF HYPERGLYCEMIA AND TUMOR SUBCLASS ON SURVIVAL IN IDH-WILDTYPE GLIOBLASTOMA},
      volume = {23},
      doi = {10.1093/neuonc/noab196.602},
      year = {2021}
    }

2020

1. A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models

   High-Dimensional Statistics, Graphical Models

   Jianqing Fan, Yang Feng, and Lucy Xia

   Journal of Econometrics, 2020

   Bib PDF

   @article{fan2020projection,
     author = {Fan, Jianqing and Feng, Yang and Xia, Lucy},
     journal = {Journal of Econometrics},
     title = {A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models},
     year = {2020},
   }

2. On the sparsity of Mallows model averaging estimator

   High-Dimensional Statistics, Model Averaging

   Yang Feng, Qingfeng Liu, and Ryo Okui

   Economics Letters, 2020

   Bib PDF

   @article{feng2020sparsity,
     author = {Feng, Yang and Liu, Qingfeng and Okui, Ryo},
     journal = {Economics Letters},
     pages = {108916},
     publisher = {North-Holland},
     title = {On the sparsity of Mallows model averaging estimator},
     volume = {187},
     year = {2020},
   }

3. On the estimation of correlation in a binary sequence model

   Network Analysis

   Haolei Weng, and Yang Feng

   Journal of Statistical Planning and Inference, 2020

   Bib PDF

   @article{weng2020estimation,
     author = {Weng, Haolei and Feng, Yang},
     journal = {Journal of Statistical Planning and Inference},
     pages = {123--137},
     publisher = {North-Holland},
     title = {On the estimation of correlation in a binary sequence model},
     volume = {207},
     year = {2020},
   }

4. Neyman-Pearson classification: parametrics and sample size requirement

   High-Dimensional Statistics, Neyman-Pearson Classification

   Xin Tong, Lucy Xia, Jiacheng Wang, and Yang Feng

   Journal of Machine Learning Research, 2020

   Bib PDF

   @article{tong2020neyman,
     author = {Tong, Xin and Xia, Lucy and Wang, Jiacheng and Feng, Yang},
     journal = {Journal of Machine Learning Research},
     title = {Neyman-Pearson classification: parametrics and sample size requirement},
     year = {2020},
   }

5. Nested Model Averaging on Solution Path for High-dimensional Linear Regression

   High-Dimensional Statistics, Model Averaging

   Yang Feng, and Qingfeng Liu

   Stat, 2020

   DOI Bib

   @article{feng2020nested,
     author = {Feng, Yang and Liu, Qingfeng},
     journal = {Stat},
     title = {Nested Model Averaging on Solution Path for High-dimensional Linear Regression},
     year = {2020},
     doi = {10.1002/sta4.317}
   }

6. Accounting for incomplete testing in the estimation of epidemic parameters

   Epidemiology

   Rebecca A Betensky, and Yang Feng

   International Journal of Epidemiology, 2020

   Bib PDF

   @article{betensky2020accounting,
     author = {Betensky, Rebecca A and Feng, Yang},
     journal = {International Journal of Epidemiology},
     publisher = {Cold Spring Harbor Laboratory Press},
     title = {Accounting for incomplete testing in the estimation of epidemic parameters},
     year = {2020},
   }

7. Analytical performance of lateral flow immunoassay for SARS-CoV-2 exposure screening on venous and capillary blood samples

   Margaret A Black, Guomiao Shen, Xiaojun Feng, Wilfredo Garcia Beltran, Yang Feng, and 6 more authors

   J Immunol Methods, 2020

   DOI Bib

   @article{black2020analytical,
     author = {Black, Margaret A and Shen, Guomiao and Feng, Xiaojun and Beltran, Wilfredo Garcia and Feng, Yang and Vasudevaraja, Varshini and Allison, Douglas and Lin, Lawrence H and Gindin, Tatyana and Astudillo, Michael and others},
     journal = {J Immunol Methods},
     publisher = {Cold Spring Harbor Laboratory Press},
     title = {Analytical performance of lateral flow immunoassay for SARS-CoV-2 exposure screening on venous and capillary blood samples},
     doi = {10.1016/j.jim.2020.112909},
     year = {2020}
   }

2019

1. Regularization after retention in ultrahigh dimensional linear regression models

   High-Dimensional Statistics

   Haolei Weng, Yang Feng, and Xingye Qiao

   Statistica Sinica, 2019

   Bib PDF

   @article{weng2019regularization,
     author = {Weng, Haolei and Feng, Yang and Qiao, Xingye},
     journal = {Statistica Sinica},
     title = {Regularization after retention in ultrahigh dimensional linear regression models},
     year = {2019},
   }

2. The restricted consistency property of leave-n_v-out cross-validation for high-dimensional variable selection

   High-Dimensional Statistics

   Yang Feng, and Yi Yu

   Statistica Sinica, 2019

   Bib PDF

   @article{feng2019restricted,
     author = {Feng, Yang and Yu, Yi},
     journal = {Statistica Sinica},
     pages = {1607--1630},
     title = {The restricted consistency property of leave-$n\_v$-out cross-validation for high-dimensional variable selection},
     volume = {29},
     year = {2019},
   }

3. Likelihood adaptively modified penalties

   High-Dimensional Statistics

   Yang Feng, Tengfei Li, and Zhiliang Ying

   Applied Stochastic Models in Business and Industry, 2019

   Bib PDF

   @article{feng2019likelihood,
     author = {Feng, Yang and Li, Tengfei and Ying, Zhiliang},
     journal = {Applied Stochastic Models in Business and Industry},
     number = {2},
     pages = {330--353},
     publisher = {Wiley Online Library},
     title = {Likelihood adaptively modified penalties},
     volume = {35},
     year = {2019},
   }

4. A kronecker product model for repeated pattern detection on 2d urban images

   Computer Vision

   Juan Liu, Emmanouil Z Psarakis, Yang Feng, and Ioannis Stamos

   IEEE transactions on pattern analysis and machine intelligence, 2019

   Bib PDF Supp

   @article{liu2019kronecker,
     author = {Liu, Juan and Psarakis, Emmanouil Z and Feng, Yang and Stamos, Ioannis},
     journal = {IEEE transactions on pattern analysis and machine intelligence},
     title = {A kronecker product model for repeated pattern detection on 2d urban images},
     year = {2019},
     number = {9},
     pages = {2266--2272},
     volume = {41},
     publisher = {IEEE},
   }

2018

1. SIS: An R Package for Sure Independence Screening in Ultrahigh Dimensional Statistical Models

   High-Dimensional Statistics

   Diego Franco Saldana, and Yang Feng

   Journal of Statistical Software, 2018

   Bib PDF

   @article{saldana2018sis,
     author = {Saldana, Diego Franco and Feng, Yang},
     journal = {Journal of Statistical Software},
     number = {2},
     pages = {1--25},
     title = {SIS: An R Package for Sure Independence Screening in Ultrahigh Dimensional Statistical Models},
     volume = {83},
     year = {2018},
   }

2. Penalized weighted least absolute deviation regression

   High-Dimensional Statistics

   Xiaoli Gao, and Yang Feng

   Statistics and Its Interface, 2018

   Bib PDF

   @article{gao2018penalized,
     author = {Gao, Xiaoli and Feng, Yang},
     journal = {Statistics and Its Interface},
     pages = {79--89},
     publisher = {http://dx.doi.org/10.4310/SII.2018.v11.n1.a7},
     title = {Penalized weighted least absolute deviation regression},
     volume = {11},
     year = {2018},
   }

3. Nonparametric Independence Screening via Favored Smoothing Bandwidth

   High-Dimensional Statistics

   Yang Feng, Yichao Wu, and Leonard Stefanski

   Journal of Statistical Planning and Inference, 2018

   Bib PDF

   @article{feng2018nonparametric,
     author = {Feng, Yang and Wu, Yichao and Stefanski, Leonard},
     journal = {Journal of Statistical Planning and Inference},
     title = {Nonparametric Independence Screening via Favored Smoothing Bandwidth},
     year = {2018},
   }

4. Neyman-Pearson classification algorithms and NP receiver operating characteristics

   High-Dimensional Statistics, Neyman-Pearson Classification

   Xin Tong, Yang Feng, and Jingyi Jessica Li

   Science Advances, 2018

   Bib PDF

   @article{tong2018neyman,
     author = {Tong, Xin and Feng, Yang and Li, Jingyi Jessica},
     journal = {Science Advances},
     title = {Neyman-Pearson classification algorithms and NP receiver operating characteristics},
     year = {2018},
     number = {2},
     pages = {eaao1659},
     volume = {4},
     publisher = {American Association for the Advancement of Science},
   }

5. Model selection for high-dimensional quadratic regression via regularization

   High-Dimensional Statistics

   Ning Hao, Yang Feng, and Hao Helen Zhang

   Journal of the American Statistical Association, 2018

   Bib PDF

   @article{hao2018model,
     author = {Hao, Ning and Feng, Yang and Zhang, Hao Helen},
     journal = {Journal of the American Statistical Association},
     title = {Model selection for high-dimensional quadratic regression via regularization},
     year = {2018},
     number = {522},
     pages = {615--625},
     volume = {113},
     publisher = {Taylor \& Francis},
   }

6. A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

   Slim Fourati, Aarthi Talla, Mehrad Mahmoudian, Joshua G Burkhart, Riku Klén, and 6 more authors

   Nature communications, 2018

   Bib PDF

   @article{fourati2018crowdsourced,
     author = {Fourati, Slim and Talla, Aarthi and Mahmoudian, Mehrad and Burkhart, Joshua G and Kl{\'e}n, Riku and Henao, Ricardo and Yu, Thomas and Ayd{\i}n, Zafer and Yeung, Ka Yee and Ahsen, Mehmet Eren and others},
     journal = {Nature communications},
     number = {1},
     pages = {4418},
     publisher = {Nature Publishing Group UK London},
     title = {A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection},
     volume = {9},
     year = {2018}
   }

2017

1. How many communities are there?

   Network Analysis

   D Franco Saldana, Yi Yu, and Yang Feng

   Journal of Computational and Graphical Statistics, 2017

   Bib PDF

   @article{saldana2017many,
     author = {Saldana, D Franco and Yu, Yi and Feng, Yang},
     journal = {Journal of Computational and Graphical Statistics},
     number = {1},
     pages = {171--181},
     publisher = {Taylor \& Francis},
     title = {How many communities are there?},
     volume = {26},
     year = {2017},
   }

2. Binary switch portfolio

   Tengfei Li, Kani Chen, Yang Feng, and Zhiliang Ying

   Quantitative Finance, 2017

   Bib PDF

   @article{li2017binary,
     author = {Li, Tengfei and Chen, Kani and Feng, Yang and Ying, Zhiliang},
     journal = {Quantitative Finance},
     number = {5},
     pages = {763--780},
     publisher = {Routledge},
     title = {Binary switch portfolio},
     volume = {17},
     year = {2017}
   }

3. Post selection shrinkage estimation for high-dimensional data analysis

   High-Dimensional Statistics

   Xiaoli Gao, SE Ahmed, and Yang Feng

   Applied Stochastic Models in Business and Industry, 2017

   Bib PDF

   @article{gao2017post,
     author = {Gao, Xiaoli and Ahmed, SE and Feng, Yang},
     journal = {Applied Stochastic Models in Business and Industry},
     number = {2},
     pages = {97--120},
     publisher = {John Wiley \& Sons, Ltd},
     title = {Post selection shrinkage estimation for high-dimensional data analysis},
     volume = {33},
     year = {2017},
   }

4. JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

   High-Dimensional Statistics, Network Analysis

   Jiadong Ji, Di He, Yang Feng, Yong He, Fuzhong Xue, and 1 more author

   Bioinformatics, 2017

   Bib PDF

   @article{ji2017jdinac,
     author = {Ji, Jiadong and He, Di and Feng, Yang and He, Yong and Xue, Fuzhong and Xie, Lei},
     journal = {Bioinformatics},
     number = {19},
     pages = {3080--3087},
     publisher = {Oxford University Press},
     title = {JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data},
     volume = {33},
     year = {2017},
   }

5. Regularization After Marginal Learning for Ultra-High Dimensional Regression Models

   High-Dimensional Statistics

   Yang Feng, and Mengjia Yu

   Big and Complex Data Analysis: Methodologies and Applications, 2017

   Bib PDF

   @article{feng2017regularization,
     author = {Feng, Yang and Yu, Mengjia},
     journal = {Big and Complex Data Analysis: Methodologies and Applications},
     pages = {3--28},
     publisher = {Springer International Publishing},
     title = {Regularization After Marginal Learning for Ultra-High Dimensional Regression Models},
     year = {2017},
   }

6. Rejoinder to ‘Post-selection shrinkage estimation for high-dimensional data analysis’

   High-Dimensional Statistics

   Xiaoli Gao, S Ejaz Ahmed, Yang Feng, and  others

   Applied Stochastic Models in Business and Industry, 2017

   Bib PDF

   @article{gao2017rejoinder,
     author = {Gao, Xiaoli and Ahmed, S Ejaz and Feng, Yang and others},
     journal = {Applied Stochastic Models in Business and Industry},
     number = {2},
     pages = {131--135},
     publisher = {John Wiley \& Sons},
     title = {Rejoinder to `Post-selection shrinkage estimation for high-dimensional data analysis'},
     volume = {33},
     year = {2017},
   }

7. Discussion on "Random-projection ensemble classification"

   High-Dimensional Statistics, Classification

   Yang Feng

   Journal of the Royal Statistical Society: Series B, 2017

   Bib PDF

   @article{feng2017discussion,
     author = {Feng, Yang},
     journal = {Journal of the Royal Statistical Society: Series B},
     number = {4},
     pages = {1011},
     title = {Discussion on "Random-projection ensemble classification"},
     volume = {79},
     year = {2017},
   }

2016

1. Feature Augmentation via Nonparametrics and Selection (FANS) in high-dimensional classification

   High-Dimensional Statistics, Classification

   Jianqing Fan, Yang Feng, Jiancheng Jiang, and Xin Tong

   Journal of the American Statistical Association, 2016

   Bib PDF

   @article{fan2016feature,
     author = {Fan, Jianqing and Feng, Yang and Jiang, Jiancheng and Tong, Xin},
     journal = {Journal of the American Statistical Association},
     title = {Feature Augmentation via Nonparametrics and Selection (FANS) in high-dimensional classification},
     year = {2016},
     number = {513},
     pages = {275--287},
     volume = {111},
     publisher = {Taylor \& Francis},
   }

2. A survey on Neyman-Pearson classification and suggestions for future research

   Neyman-Pearson Classification

   Xin Tong, Yang Feng, and Anqi Zhao

   Wiley Interdisciplinary Reviews: Computational Statistics, 2016

   Bib PDF

   @article{tong2016survey,
     author = {Tong, Xin and Feng, Yang and Zhao, Anqi},
     journal = {Wiley Interdisciplinary Reviews: Computational Statistics},
     number = {2},
     pages = {64--81},
     publisher = {Wiley Online Library},
     title = {A survey on Neyman-Pearson classification and suggestions for future research},
     volume = {8},
     year = {2016},
   }

3. Variable selection and prediction with incomplete high-dimensional data

   High-Dimensional Statistics

   Ying Liu, Yuanjia Wang, Yang Feng, and Melanie M Wall

   The annals of applied statistics, 2016

   Bib PDF

   @article{liu2016variable,
     author = {Liu, Ying and Wang, Yuanjia and Feng, Yang and Wall, Melanie M},
     journal = {The annals of applied statistics},
     number = {1},
     pages = {418},
     publisher = {NIH Public Access},
     title = {Variable selection and prediction with incomplete high-dimensional data},
     volume = {10},
     year = {2016},
   }

4. Neyman-Pearson classification under high-dimensional settings

   High-Dimensional Statistics, Neyman-Pearson Classification

   Anqi Zhao, Yang Feng, Lie Wang, and Xin Tong

   Journal of Machine Learning Research, 2016

   Bib PDF

   @article{zhao2016neyman,
     author = {Zhao, Anqi and Feng, Yang and Wang, Lie and Tong, Xin},
     journal = {Journal of Machine Learning Research},
     title = {Neyman-Pearson classification under high-dimensional settings},
     year = {2016},
     number = {212},
     pages = {1--39},
     volume = {17},
   }

5. Tuning-parameter selection in regularized estimations of large covariance matrices

   High-Dimensional Statistics

   Yixin Fang, Binhuan Wang, and Yang Feng

   Journal of Statistical Computation and Simulation, 2016

   Bib PDF

   @article{fang2016tuning,
     author = {Fang, Yixin and Wang, Binhuan and Feng, Yang},
     journal = {Journal of Statistical Computation and Simulation},
     number = {3},
     pages = {494--509},
     publisher = {Taylor \& Francis},
     title = {Tuning-parameter selection in regularized estimations of large covariance matrices},
     volume = {86},
     year = {2016},
   }

2015

1. Functional and Parametric Estimation in a Semi-and Nonparametric Model with Application to Mass-Spectrometry Data

   Nonparametric Statistics

   Weiping Ma, Yang Feng, Kani Chen, and Zhiliang Ying

   The international journal of biostatistics, 2015

   Bib PDF

   @article{ma2015functional,
     author = {Ma, Weiping and Feng, Yang and Chen, Kani and Ying, Zhiliang},
     journal = {The international journal of biostatistics},
     number = {2},
     pages = {285--303},
     title = {Functional and Parametric Estimation in a Semi-and Nonparametric Model with Application to Mass-Spectrometry Data},
     volume = {11},
     year = {2015},
   }

2014

1. Apple: Approximate path for penalized likelihood estimators

   High-Dimensional Statistics

   Yi Yu, and Yang Feng

   Statistics and Computing, 2014

   Bib PDF

   @article{yu2014apple,
     author = {Yu, Yi and Feng, Yang},
     journal = {Statistics and Computing},
     pages = {803--819},
     publisher = {Springer US},
     title = {Apple: Approximate path for penalized likelihood estimators},
     volume = {24},
     year = {2014},
   }

2. Modified cross-validation for penalized high-dimensional linear regression models

   High-Dimensional Statistics

   Yi Yu, and Yang Feng

   Journal of Computational and Graphical Statistics, 2014

   Bib PDF

   @article{yu2014modified,
     author = {Yu, Yi and Feng, Yang},
     journal = {Journal of Computational and Graphical Statistics},
     number = {4},
     pages = {1009--1027},
     publisher = {Taylor \& Francis},
     title = {Modified cross-validation for penalized high-dimensional linear regression models},
     volume = {23},
     year = {2014},
   }

3. Regularized principal components of heritability

   High-Dimensional Statistics

   Yixin Fang, Yang Feng, and Ming Yuan

   Computational Statistics, 2014

   Bib PDF

   @article{fang2014regularized,
     author = {Fang, Yixin and Feng, Yang and Yuan, Ming},
     journal = {Computational Statistics},
     pages = {455--465},
     publisher = {Springer Berlin Heidelberg},
     title = {Regularized principal components of heritability},
     volume = {29},
     year = {2014},
   }

2013

1. A conversation with Jianqing Fan

   Yang Feng, and Xin Tong

   ICSA Bulletin, 2013

   Bib PDF

   @article{feng2013conversation,
     author = {Feng, Yang and Tong, Xin},
     journal = {ICSA Bulletin},
     pages = {22--28},
     title = {A conversation with Jianqing Fan},
     volume = {25},
     year = {2013}
   }

2012

1. A road to classification in high dimensional space: the regularized optimal affine discriminant

   High-Dimensional Statistics, Classification

   Jianqing Fan, Yang Feng, and Xin Tong

   Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2012

   Bib PDF

   @article{fan2012road,
     author = {Fan, Jianqing and Feng, Yang and Tong, Xin},
     journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
     title = {A road to classification in high dimensional space: the regularized optimal affine discriminant},
     year = {2012},
     number = {4},
     pages = {745--771},
     volume = {74},
     publisher = {Wiley Online Library},
   }

2011

1. Nonparametric independence screening in sparse ultra-high-dimensional additive models

   High-dimensional Statistics, Nonparametric Statistics

   Jianqing Fan, Yang Feng, and Rui Song

   Journal of the American Statistical Association, 2011

   Bib PDF

   @article{fan2011nonparametric,
     author = {Fan, Jianqing and Feng, Yang and Song, Rui},
     journal = {Journal of the American Statistical Association},
     title = {Nonparametric independence screening in sparse ultra-high-dimensional additive models},
     year = {2011},
     pages = {544--557},
     volume = {106},
     publisher = {Taylor \& Francis},
   }

2010

1. High-dimensional variable selection for Cox’s proportional hazards model

   High-Dimensional Statistics

   Jianqing Fan, Yang Feng, and Yichao Wu

   In Borrowing strength: Theory powering applications–a Festschrift for Lawrence D. Brown, 2010

   Bib PDF

   @incollection{fan2010high,
     author = {Fan, Jianqing and Feng, Yang and Wu, Yichao},
     booktitle = {Borrowing strength: Theory powering applications--a Festschrift for Lawrence D. Brown},
     pages = {70--87},
     publisher = {Institute of Mathematical Statistics},
     title = {High-dimensional variable selection for Cox's proportional hazards model},
     volume = {6},
     year = {2010},
   }

2. Nonparametric estimation of genewise variance for microarray data

   Nonparametric Statistics

   Jianqing Fan, Yang Feng, and Yue S Niu

   Annals of statistics, 2010

   Bib PDF

   @article{fan2010nonparametric,
     author = {Fan, Jianqing and Feng, Yang and Niu, Yue S},
     journal = {Annals of statistics},
     title = {Nonparametric estimation of genewise variance for microarray data},
     year = {2010},
     number = {5},
     pages = {2723},
     volume = {38},
     publisher = {NIH Public Access},
   }

3. High-dimensional statistical learning and nonparametric modeling

   High-Dimensional Statistics, Nonparametric Statistics

   Yang Feng

   Princeton University, 2010

   Bib

   @phdthesis{feng2010high,
     author = {Feng, Yang},
     school = {Princeton University},
     title = {High-dimensional statistical learning and nonparametric modeling},
     year = {2010},
   }

4. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

   Leming Shi, Gregory Campbell, Wendell D Jones, Fabien Campagne, Zhining Wen, and 6 more authors

   Nature biotechnology, 2010

   Bib Website

   @article{shi2010microarray,
     author = {Shi, Leming and Campbell, Gregory and Jones, Wendell D and Campagne, Fabien and Wen, Zhining and Walker, Stephen J and Su, Zhenqiang and Chu, Tzu-Ming and Goodsaid, Federico M and Pusztai, Lajos and others},
     journal = {Nature biotechnology},
     number = {8},
     pages = {827},
     title = {The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.},
     volume = {28},
     year = {2010},
   }

2009

1. Network exploration via the adaptive LASSO and SCAD penalties

   High-dimensional Statistics, Network Analysis, Graphical Models

   Jianqing Fan, Yang Feng, and Yichao Wu

   The annals of applied statistics, 2009

   Abs Bib PDF

   Graphical models are frequently used to explore networks, such as genetic networks, among a set of variables. This is usually carried out via exploring the sparsity of the precision matrix of the variables under consideration. Penalized likelihood methods are often used in such explorations. Yet, positive-definiteness constraints of precision matrices make the optimization problem challenging. We introduce non-concave penalties and the adaptive LASSO penalty to attenuate the bias problem in the network estimation. Through the local linear approximation to the non-concave penalty functions, the problem of precision matrix estimation is recast as a sequence of penalized likelihood problems with a weighted L1 penalty and solved using the efficient algorithm of Friedman et al. (2008). Our estimation schemes are applied to two real datasets. Simulation experiments and asymptotic theory are used to justify our proposed methods.

   @article{fan2009network,
     author = {Fan, Jianqing and Feng, Yang and Wu, Yichao},
     journal = {The annals of applied statistics},
     title = {Network exploration via the adaptive LASSO and SCAD penalties},
     year = {2009},
     pages = {521--541},
     publisher = {Institute of Mathematical Statistics},
   }

2. Local quasi-likelihood with a parametric guide

   Nonparametric Statistics

   Jianqing Fan, Yichao Wu, and Yang Feng

   Annals of statistics, 2009

   Bib PDF

   @article{fan2009local,
     author = {Fan, Jianqing and Wu, Yichao and Feng, Yang},
     journal = {Annals of statistics},
     title = {Local quasi-likelihood with a parametric guide},
     year = {2009},
     number = {6B},
     pages = {4153},
     volume = {37},
     publisher = {NIH Public Access},
   }

3. Alignment of protein mass spectrometry data by integrated Markov chain shifting method

   Nonparametric Statistics

   Yang Feng, Weiping Ma, Zhanfeng Wang, Yaning Yang, and Zhiliang Ying

   Statistics and its Interface, 2009

   Bib PDF

   @article{feng2009alignment,
     author = {Feng, Yang and Ma, Weiping and Wang, Zhanfeng and Yang, Yaning and Ying, Zhiliang},
     journal = {Statistics and its Interface},
     number = {3},
     pages = {329--340},
     publisher = {International Press of Boston},
     title = {Alignment of protein mass spectrometry data by integrated Markov chain shifting method},
     volume = {2},
     year = {2009},
   }

4. Discussion on "Nonparametric Prediction in Measurement Error Models"

   Nonparametric Statistics

   Jianqing Fan, and Yang Feng

   Journal of the American Statistical Association, 2009

   Bib PDF

   @article{fan2009discussion,
     author = {Fan, Jianqing and Feng, Yang},
     journal = {Journal of the American Statistical Association},
     number = {487},
     pages = {1003--1007},
     publisher = {American Statistical Association},
     title = {Discussion on "Nonparametric Prediction in Measurement Error Models"},
     volume = {104},
     year = {2009},
   }