BIMSA > Yuhong Yang
Professor Yuhong Yang

Affiliation: YMSC, BIMSA

Group: Statistics, Probability and Data Science

Email: yangyuhong@bimsa.cn

Education Experience

  • 1993 - 1996      Yale University      Statistics      Doctor
  • 1988 - 1993      University of Illinois at Urbana-Champaign      Statistics      Master
  • 1984 - 1988      University of Science and Technology of China      Mathematica      Bachelor

Publication

  • [1] J Peng, Y Li, Y Yang, On optimality of Mallows model averaging, Journal of the American Statistical Association, 1, 1-12 (2024)
  • [2] J Zhang, Z Chen, Y Yang, W Xu, Variable importance based interaction modelling with an application on initial spread of COVID-19 in China, Journal of the Royal Statistical Society Series C: Applied Statistics, 73(5) (2024)
  • [3] X Tang, J Zhang, Y He, X Zhang, Z Lin, S Partarrieu, EB Hanna, Z Ren et al., Explainable multi-task learning for multi-modality biological data analysis, Nature communications, 14(1) (2023)
  • [4] J Zhang, Y Yang, J Ding, Information criteria for model selection, Wiley Interdisciplinary Reviews: Computational Statistics, 15(5) (2023)
  • [5] E Diao, G Wang, J Zhan, Y Yang, J Ding, V Tarokh, Pruning deep neural networks from a sparsity perspective, arXiv preprint arXiv:2302.05601 (2023)
  • [6] J Zhang, J Ding, Y Yang, Is a classification procedure good enough?—A goodness-of-fit assessment tool for classification learning, Journal of the American Statistical Association, 118(542), 1115-1125 (2023)
  • [7] Z Zhan, Y Li, Y Yang, C Lin, Model averaging for semiparametric varying coefficient quantile regression models, Annals of the Institute of Statistical Mathematics, 75(4), 649-681 (2023)
  • [8] Z Chen, J Liao, W Xu, Y Yang, Multifold cross-validation model averaging for generalized additive partial linear models, Journal of Computational and Graphical Statistics, 32(4), 1649-1659 (2023)
  • [9] G Wang, J Ding, Y Yang, Regression with set-valued categorical predictors, Statistica Sinica, 33(4), 2545-2560 (2023)
  • [10] J Zhang, J Ding, Y Yang, Targeted cross-validation, Bernoulli, 29(1), 377-402 (2023)
  • [11] B Zhao, Y Yang, Minimax rates of convergence for nonparametric location-scale models, Other (2023)
  • [12] C Lin, J Peng, Y Qin, Y Li, Y Yang, Optimal integrating learning for split questionnaire design type data, Journal of Computational and Graphical Statistics, 32(3), 1009-1023 (2023)
  • [13] J Peng, Y Yang, On improvability of model selection by model averaging, Journal of Econometrics, 229(2), 246-262 (2022)
  • [14] W Qian, CA Rolling, G Cheng, Y Yang, Combining forecasts for universally optimal performance, International Journal of Forecasting, 38(1), 193-208 (2022)
  • [15] C Ye, L Zhang, M Han, Y Yu, B Zhao, Y Yang, Combining predictions of auto insurance claims, Econometrics, 10(2) (2022)
  • [16] Z Zhan, Y Yang, Profile electoral college cross-validation, Information Sciences, 586, 24-40 (2022)
  • [17] Y Yu, Y Yang, Y Yang, Performance assessment of high-dimensional variable identification, Statistica Sinica, 32(2), 695-718 (2022)
  • [18] X Wang, J Zhang, M Hong, Y Yang, J Ding, Parallel assisted learning, IEEE Transactions on Signal Processing, 70, 5848-5858 (2022)
  • [19] W Yang, G Wang, J Ding, Y Yang, A theoretical understanding of neural network compression from sparse linear approximation, arXiv preprint arXiv:2206.05604 (2022)
  • [20] Z Chen, J Zhang, W Xu, Y Yang, Consistency of BIC model averaging, Statistica Sinica, 32, 635-640 (2022)
  • [21] J Zhang, J Ding, Y Yang, Supplementary Material for “Is a Classification Procedure Good Enough?-A Goodness-of-Fit Assessment Tool for Classification Learning” (2022)
  • [22] C Ye, L Zhang, M Han, Y Yu, B Zhao, Y Yang, Combining Predictions of Auto Insurance Claims. Econometrics 10: 19, s Note: MDPI stays neutral with regard to jurisdictional claims in published … (2022)
  • [23] Y Chen, Y Yang, The one standard error rule for model selection: Does it work?, Stats, 4(4), 868-892 (2021)
  • [24] W Yang, Y Yang, A stabilized dense network approach for high-dimensional prediction, International Joint Conference on Neural Networks, 1-8 (2021)
  • [25] Y Li, R Li, Y Qin, C Lin, Y Yang, Robust group variable screening based on maximum Lq‐likelihood estimation, Statistics in Medicine, 40(30), 6818-6834 (2021)
  • [26] S Arya, Y Yang, To update or not to update? Delayed nonparametric bandits with randomized allocation, Stat, 10(1) (2021)
  • [27] Y Zhang, C Rolling, Y Yang, Estimating and forecasting dynamic correlation matrices: A nonlinear common factor approach, Journal of Multivariate Analysis, 183, 104710 (2021)
  • [28] J Ding, Y Yang, V Tarokh, 12 Fundamental Limits in Model Selection for Modern Data Analysis, Information-Theoretic Methods in Data Science, 359 (2021)
  • [29] S Arya, Y Yang, Randomized allocation with nonparametric estimation for contextual multi-armed bandits with delayed rewards, Statistics & Probability Letters, 164 (2020)
  • [30] W Yang, Z Cao, Q Chen, Y Yang, G Yang, Confidence calibration on multiclass classification in medical imaging, IEEE International Conference on Data Mining (ICDM), 1364-1369 (2020)
  • [31] C Zheng, D Ferrari, Y Yang, Model selection confidence sets by likelihood ratio testing, Statistica Sinica, 29(2), 827-851 (2019)
  • [32] W Qian, CA Rolling, G Cheng, Y Yang, On the forecast combination puzzle, Econometrics, 7(3) (2019)
  • [33] CA Rolling, Y Yang, D Velez, Combining estimates of conditional treatment effects, Econometric Theory, 35(6), 1089-1110 (2019)
  • [34] C Ye, Y Yang, High-dimensional adaptive minimax sparse estimation with interactions, IEEE Transactions on Information Theory, 65(9), 5367-5379 (2019)
  • [35] C Lu, Y Yang, On assessing binary regression models based on ungrouped data, Biometrics, 75(1), 5-12 (2019)
  • [36] G Yang, Y Yang, Minimax-rate adaptive nonparametric regression with unknown correlations of errors, Science China Mathematics, 62, 227-244 (2019)
  • [37] J Ding, V Tarokh, Y Yang, Model selection techniques: An overview, IEEE Signal Processing Magazine, 35(6), 16-34 (2018)
  • [38] C Ye, Y Yang, Y Yang, Sparsity oriented importance learning for high-dimensional linear regression, Journal of the American Statistical Association, 113(524), 1797-1812 (2018)
  • [39] J Ding, V Tarokh, Y Yang, Bridging AIC and BIC: a new criterion for autoregression, IEEE Transactions on Information Theory, 64(6), 4024-4043 (2017)
  • [40] C Lu, K Liu, L Li, Y Yang, Sensitivity of measuring the progress in financial risk protection to survey design and its socioeconomic and demographic determinants: a case study in Rwanda, Social Science & Medicine, 178, 11-18 (2017)
  • [41] J Cockreham, F Gao, Y Yang, Metric entropy of 𝑞-hulls in Banach spaces of type-𝑝, Proceedings of the American Mathematical Society, 145(12), 5205-5214 (2017)
  • [42] F Lv, G Yang, J Wu, C Liu, Y Yang, Anomaly Detection for Categorical Observations Using Latent Gaussian Process, Neural Information Processing (2017)
  • [43] C Ye, Y Yang, Y Yang, Supplemental Materials for “Sparsity Oriented Importance Learning for High-dimensional Linear Regression” (2017)
  • [44] Y Yang, Cross-Validation for Optimal and Reproducible Statistical Learning (2017)
  • [45] W Qian, Y Yang, Kernel estimation and model combination in a bandit problem with covariates, Journal of Machine Learning Research, 17(149), 1-37 (2016)
  • [46] W Qian, Y Yang, Randomized allocation with arm elimination in a bandit problem with covariates (2016)
  • [47] WYY Yang, Toward an objective and reproducible model choice via variable selection deviation, Biometrics (2016)
  • [48] J Ding, V Tarokh, Y Yang, Optimal variable selection in regression models (2016)
  • [49] Y Zhang, Y Yang, Cross-validation for selecting a model selection procedure, Journal of Econometrics, 187, 95-112 (2015)
  • [50] D Ferrari, Y Yang, Confidence sets for model selection by F-testing, Statistica Sinica, 1637-1658 (2015)
  • [51] G Cheng, Y Yang, Forecast combination with outlier protection, International journal of forecasting, 31(2), 223-237 (2015)
  • [52] X Zhu, Y Yang, Variable selection after screening: with or without data splitting?, Computational Statistics, 30, 191-203 (2015)
  • [53] G Cheng, S Wang, Y Yang, Forecast combination under heavy-tailed errors, Econometrics, 3(4), 797-824 (2015)
  • [54] Y Nan, Y Yang, Y Yang, C Ye, MY Yang, Package ‘glmvsd’ (2015)
  • [55] CA Rolling, Y Yang, Model selection for estimating treatment effects, Journal of the Royal Statistical Society Series B: Statistical Methodology (2014)
  • [56] Y Nan, Y Yang, Variable selection diagnostics measures for high-dimensional regression, Journal of Computational and Graphical Statistics, 23(3), 636-656 (2014)
  • [57] Z Wang, S Paterlini, F Gao, Y Yang, Adaptive minimax regression estimation over sparse lq-hulls, The Journal of Machine Learning Research, 15(1), 1675-1711 (2014)
  • [58] W Qian, Y Yang, Model selection via standard error adjusted adaptive lasso, Annals of the Institute of Statistical Mathematics, 65, 295-318 (2013)
  • [59] GA Rempala, Y Yang, On permutation procedures for strong control in multiple testing with gene expression data, Statistics and its interface, 6(1) (2013)
  • [60] F Gao, CK Ing, Y Yang, Metric entropy and sparse linear approximation of ℓq-hulls for 0< q≤ 1, Journal of Approximation Theory, 166, 42-55 (2013)
  • [61] S Liu, Y Yang, Mixing partially linear regression models, Sankhya A, 75, 74-95 (2013)
  • [62] X Wei, Y Yang, Robust forecast combinations, Journal of Econometrics, 166(2), 224-236 (2012)
  • [63] S Liu, Y Yang, Combining models in longitudinal data analysis, Annals of the Institute of Statistical Mathematics, 64, 233-254 (2012)
  • [64] Z SU, G ZHU, X CHEN, Y YANG, Supplementary material for “Sparse Envelope Model: Efficient Estimation and Response Variable Selection in Multivariate Linear Regression”, Biometrika, 99(1), 1-21 (2012)
  • [65] W Liu, Y Yang, Kernal Estimation in a Bandit Problem with Covariates, University of Minnesota (2012)
  • [66] W Liu, Y Yang, Parametric or nonparametric? A parametricness index for model selection (2011)
  • [67] Z Wang, S Paterlini, F Gao, Y Yang, Adaptive Minimax Estimation over Sparse$\ell_q$-Hulls, Other (2011)
  • [68] L Chen, Y Yang, Combining statistical procedures, High-dimensional data analysis, 275-298 (2011)
  • [69] Y Yang, G Cheng, EB Laber, SA Murphy, Discussion of" Adaptive confidence intervals for the test error in classification", JASA, 106, 924-931 (2011)
  • [70] D Ferrari, Y Yang, Maximum lq-likelihood method, Annals of Statistics, 38, 573-583 (2010)
  • [71] D Ferrari, Y Yang, Maximum Lq-likelihood estimation, Annals of Statistics, 38, 753-783 (2009)
  • [72] K Shan, Y Yang, Combining regression quantile estimators, Statistica Sinica, 1171-1191 (2009)
  • [73] Y Yang, Localized model selection for regression, Econometric Theory, 24(2), 472-492 (2008)
  • [74] Y Yang, Review of “Elements of Information Theory”, by T. Cover and J. Thomas, Wiley, JASA, 103 (2008)
  • [75] Y Yang, Consistency of cross validation for comparing regression procedures (2007)
  • [76] Y Yang, Prediction/estimation with simple linear models: Is it really that simple?, Econometric Theory, 23(1), 1-36 (2007)
  • [77] L Chen, P Giannakouros, Y Yang, Model combining in factorial data analysis, Journal of Statistical Planning and Inference, 137(9), 2920-2934 (2007)
  • [78] Z Chen, Y Yang, Time Series Models for Forecasting: Testing or Combining?, Studies in Nonlinear Dynamics & Econometrics, 11(1) (2007)
  • [79] D Ferrari, Y Yang, Estimation of tail probability via the maximum Lq-likelihood method, University of Minnesota (2007)
  • [80] Y Yang, How powerful can any regression learning procedure be, Proceedings of the 11th International Conference on Artificial Intelligence (2007)
  • [81] Y Yang, Comparing learning methods for classification, Statistica Sinica, 635-657 (2006)
  • [82] Y Yang, Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation, Biometrika, 92(4), 937-950 (2005)
  • [83] Z Yuan, Y Yang, Combining linear regression models: When and How?, Journal of the American Statistical Association, 100(472) (2005)
  • [84] Y Yang, Review of “Information Theory, Inference, and Learning Algorithms”, by DJC MacKay, Cambridge University Press, JASA, 100, 1461-1462 (2005)
  • [85] H Zou, Y Yang, Combining time series models for forecasting, International Journal of Forecasting, 20(1), 69-84 (2004)
  • [86] Y Yang, Combining forecasting procedures: some theoretical results, Econometric Theory, 20(1), 176-222 (2004)
  • [87] Y Yang, Aggregating regression procedures to improve performance, Bernoulli, 25-47 (2004)
  • [88] Z Chen, Y Yang, Assessing forecast accuracy measures, Preprint Series (2004)
  • [89] M Paik, Y Yang, Combining nearest neighbor classifiers versus cross-validation selection, Statistical applications in genetics and molecular biology, 3(1) (2004)
  • [90] Y Yang, Review of “Nonlinear Estimation and Classification”, by DD Denison, MH Hansen, CC Holmes, B. Mallick, and B. Yu (eds.), JASA, 99, 561-561 (2004)
  • [91] Y Yang, Regression with multiple candidate models: selecting or mixing?, Statistica Sinica, 783-809 (2003)
  • [92] Y Yang, D Zhu, Randomized allocation with nonparametric estimation for a multi-armed bandit problem with covariates, The Annals of Statistics, 30(1), 100-121 (2002)
  • [93] J Opsomer, Y Wang, Y Yang, Nonparametric regression with correlated errors, Statistical science, 134-153 (2001)
  • [94] Y Yang, Adaptive regression by mixing, Journal of the American Statistical Association, 96(454), 574-588 (2001)
  • [95] Y Yang, Nonparametric regression with dependent errors (2001)
  • [96] Y Yang, Minimax rate adaptive estimation over continuous hyper-parameters, IEEE Transactions on Information Theory, 47(5), 2081-2085 (2001)
  • [97] Y Yang, Combining different procedures for adaptive regression, Journal of multivariate analysis, 74(1), 135-161 (2000)
  • [98] Y Yang, Mixing strategies for density estimation, Annals of Statistics, 75-87 (2000)
  • [99] Y Yang, Adaptive estimation in pattern recognition by combining different procedures, Statistica Sinica, 1069-1089 (2000)
  • [100] Y Yang, Comment on “Finite sample performance guarantees of fusers for function estimators”[Information Fusion 1 (2000) 35–44], Information Fusion, 1(2), 99-100 (2000)
  • [101] Y Yang, A Barron, Information-theoretic determination of minimax rates of convergence, Annals of Statistics, 1564-1599 (1999)
  • [102] Y Yang, Minimax nonparametric classification. I. Rates of convergence, IEEE Transactions on Information Theory, 45(7), 2271-2284 (1999)
  • [103] Y Yang, Model selection for nonparametric regression, Statistica Sinica, 475-499 (1999)
  • [104] Y Yang, Minimax nonparametric classification. II. Model selection for adaptation, IEEE Transactions on Information Theory, 45(7), 2285-2292 (1999)
  • [105] Y Yang, Aggregating regression procedures for a better performance (1999)
  • [106] Y Yang, AR Barron, An asymptotic property of model selection criteria, IEEE Transactions on Information Theory, 44(1), 95-116 (1998)
  • [107] Y Yang, On adaptive function estimation, Iowa State University. Department of Statistics. Statistical Laboratory (1997)
  • [108] Y Yang, Minimax optimal density estimation, Yale University (1996)
  • [109] A Barron, Y Yang, B Yu, Asymptotically optimal function estimation by minimum complexity criteria, Proceedings of 1994 IEEE International Symposium on Information Theory, 38 (1994)
  • [110] J Zhang, J Ding, Y Yang, A binary regression adaptive goodness-of-fit Test (BAGofT) (1911)
  • [111] J Du, Y Yang, J Ding, Adaptive Continual Learning: Rapid Adaptation and Knowledge Refinement
  • [112] J Ding, Y Yang, Statistica Sinica Preprint No: SS-2021-0332
  • [113] G Wang, J Ding, Y Yang, S1 Table of notations
  • [114] J Zhang, W Xu, Y Yang, Statistica Sinica Preprint No: SS-2021-0145
  • [115] Y Yang, Y Yang, Statistica Sinica Preprint No: SS-2019-0210
  • [116] Y Yu, Y Yang, Y Yang, Supplemental Materials for “Performance Assessment of High-dimensional Variable Identification”
  • [117] Y Yang, Z Chen, ADAPTIVE FORECAST COMBINING
  • [118] Y Nan, Y Yang, Supplementary Material: Additional Numerical Results
  • [119] D FERRARI, Y YANG, S1 Proof of Theorem 2.3
  • [120] Y Yang, G Cheng, Discussion of “Adaptive confidence intervals for the test error in classification” by Eric B. Laber and Susan A. Murphy
  • [121] Y Yang, Nonparametric Regression and Prediction with Dependent Errors (Running Title: Regression and Prediction under Dependence)
  • [122] Y Yang, Nonparametric Classification: Rate of Convergence and Adaptation
  • [123] W Liu, Y Yang, SUPPLEMENT TO “PARAMETRIC OR NONPARAMETRIC? A PARAMETRICNESS INDEX FOR MODEL SELECTION”

 

Update Time: 2025-07-23 17:51:05