英文发表 |
2024 |
Ma, Y., Chen, L., andWang, H.(2024)Optimal Subsampling Bootstrap for Massive Data, Journal of Business & Economic Statistics,42(1): 174-186. Ren, Y., Li, Z., Zhu,X., Gao, Y., andWang, H.Distributedestimation andinference forspatialautoregressionmodel withlargescalenetworks,Journal of Econometrics,To appear. Gao, T., Liu, J., Pan, R., and Wang, H. (2024) Citation counts prediction of statistical publications based on multi-layer academic networks via neural network model, Expert Systems with Applications, 238: 121634. Wu, S.,Huang,D., andWang, H.Quasi-newtonupdating forlarge-scaledistributedlearning, Journal of Royal Statistical Society: Series B,To appear. Qi, H., Zhu, X., andWang, H.A random projection method for large-sacle community detection, Statistics and Its Interface, To appear. Gao, T., Pan, R., Zhang, J., andWang, H.Community detection in temporal citation network via a tensor-based approach, Statistics and Its Interface, To appear. Li, Y., Chen, L., Li, D., andWang, H.Estimating extreme value index by subsampling for massive datasets with heavy-tailed distributions, Statistics and Its Interface, To appear. Zeng, Q., Zhu, Y., Zhu, X., Wang, F., Zhao, W., Sun, S., Su, M., andWang, H.Improved naive Bayes with mislabeled data, Statistics and Its Interface, To appear. Li, X., Zhu, X., andWang, H.Distributed logistic regression for massive data with rare events, Statistica Sinica, To appear. Xu, K., Zhu, Y., Liu, Y.,andWang, H.CluBear: Asubsamplingpackage forinteractivestatisticalanalysiswithmassiveaata onasinglemachine, Communications in Statistics - Simulation and Computation,To appear. |
2023 |
Jing Zhou, Shan Leng, Fang Wang, and Hansheng Wang.(2023)Automatic discovery of controversial legal judgments by an entropy-based measurement (S). In The 35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023, KSIR Virtual Conference Center, USA, July 1-10, 2023, pages 82–85. Zhou, J., Hu, B., Feng, W., Zhang, Z., Fu, X., Shao, H.,Wang, H., Jin, L., Ai, S.,andJi, Y.(2023)Anensembledeeplearningmodel forriskstratification ofinvasivelungadenocarcinomausingthin-slice CT, NPJDigitalMedicine,6: 119. Gao, Y., Zhu, X., Qi, H., Li, G., Zhang, R., andWang, H.(2023)An asymptotic analysis of random partition based minibatch momentum methods for linear regression models, Journal of Computational and Graphical Statistics,32(3), 1083-1096. Gao, Y., Zhang, R., andWang, H.(2023) On the asymptotic properties of a bagging estimator with a massive dataset, Stat,11(1): e485. Qi, H., Wang, F., andWang, H.(2023)Statistical analysis of fixed mini-batch gradient descent estimator, Journal of Computational and Graphical Statistics,34(4): 1348-1360. Wu, S., Huang,D., andWang, H.(2023)Network gradient descent algorithm for decentralized federated learning, Journal of Business and Economic Statistics,41(3): 806-818. Ma, Y., Guo, S., andWang, H.(2023) Sparse spatio-temporal autoregressions by profiling and bagging, Journal of Econometrics, 232(1): 132-147. Wu, S., Zhu, X., and Wang, H. (2023) Subsampling and jackknifing: A practically convenient solution for large data anlaysis with liited computational resources, Statistica Sinica, 33: 1-24. Liu, J., Zhou, J., Lan, W., andWang, H.(2023)Spatial dynamic panel models with missing data, Stat, 12(1): e585. Zhou, J., Jing, X., Liu, M., andWang, H.(2023)Compressing the embedding matrix by a dictionary screening Approach in text classification, The 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2023), LNAI 13935:457–468. Zhang, J., Cai, B., Zhu, X., andWang, H., Xu, G. and Guan. Y. (2023) Learning human activity patterns using clustered point processes with active and inactive states, Journal of Business & Economic Statistics,41(2):388-398. Li, X.,Wang, F.,Lan, W., andWang, H.(2023)Subnetworkestimation forspatialautoregressivemodels inlarge-scalenetworks,Electronic Journal of Statistics,17:1768–1805. Pan, R., Zhu, Y., Guo, B., Zhu, X., andWang, H.(2023)A sequential addressing subsampling method for massive data analysis under memory constraint, IEEE Transactions on Knowledge and Data Engineering,35(9): 9502-9513. |
2022 |
Zhang, Y., Pan, R.,Wang, H., and Su, H. (2022) Community detection in attributed collaboration network for statisticians, Stat, 12(1): e507. Zhang, R., Zhou, J., Lan, W., andWang, H.(2022) A Case Study on the Shareholder Network Effect of Stock Market Data: A SARMA Approach, Science China Mathematics, 65(11): 2219-2242. Wang, F., Huang, D., Gao, T., Wu, S., andWang, H.(2022) Sequential one-step estimator by subsampling for customer churn analysis with massive datasets, Journal of Royal Statistical Society, Series C, 71(5): 1753-1786 Pan, R., Ren, T., Guo, B., Li, F., Li, G., andWang, H.(2022) A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating, Journal of Business and Economic Statistics, 40(4): 1691-1700. Pan, R., Chang, X., Zhu, X., andWang, H.(2022) Link prediction via latent space logistic regression, Statistics and Its Interface, 15(3): 267-282. Gao, Y., Liu, W.,Wang, H., Wang, X., Yan, Y., and Zhang, R., (2022) A review of distributed statistical inference, Statistical Theory and Related Fields, 2(6): 89-99. Zhao, J., Liu, X.,Wang, H.and Leng, C.,(2022) Dimension reduction for covariates in network data, Biometrika, 109(1): 85-102. Zhu, X., Pan, R., Wu, S., andWang, H.(2022) Feature Screening for Massive Data Analysis by Subsampling, Journal of Business & Economic Statistics, 4(40): 1892-1903. Zhou, J., Liu, J, Wang, F., andWang, H.(2022) Autoregression model with spatial dependence and missing data, Journal of Business & Economic Statistics, 1(40): 28-40. Zhou, J., Lan, W.,and Wang, H.(2022) Asymptotic covariance estimation by Gaussian random perturbation, Computational Statistics & Data Analysis, 171:107459. Qi, H., Zhou, J., andWang, H.(2022) A note on factor normalization for deep neural network models, Scientific Reports, 12(1):5909. DOI: 10.1038/s41598-022-09910-6. Song, X., Zhang, Y., Pan, R., andWang, H.(2022) Link prediction for statistical collaboration networks incorporating institutes and research interests, IEEE Access, DOI: 10.1109/ACCESS.2022.3210129. |
2021 |
Xu, K., Liu, H., Wang,F., andWang, H.(2021) This crime is not that crime —Classification and evaluation of four common crimes, Law Probability and Risk, 3(20): 135-152. Zhu, X., Li, F., andWang, H.Least-square approximation for a distributed system, (2021) Journal of Computational and Graphical Statistics, 4(30):1004-1018. Zhou, J., Qi, H., Chen, Y., andWang, H.（2021）Progressive principle component analysis for compressing deep convolutional neural networks, Neurocomputing, 440: 197-206. Zhu, X., Pan, R., Zhang, Y., Chen, Y., Mi, W., andWang, H.(2021) Information diffusion with network structures, Statistics and Its Interface, 14(02): 115–129. Huang, D., Zhu, X., Li, R., andWang, H.(2021) Feature screening for network autoregression model, Statistica Sinica, 31: 1239-1259. Wang, G., Liang, B.,Wang, H., Zhang, B., and Xie, B., (2021) Dimension reduction for functional regression with a binary response,Statistical Papers, 62: 193–208. Zhu, Y., Huang, D., Gao, Y., Wu, R., Chen, Y., Zhang, B., and Wang, H. (2021) Automatic, dynamics, and nearly optimal learning rate specification via local quadratic approximation. Neural Networks, 141: 11-29. Wang, F., Liu, J., andWang, H.Sequential text-term selection in vector space models, Journal of Business and Economic Statistics, 39(1): 82-97. Wang, F., Zhu, Y., Huang, D., Qi, H. andWang, H.(2021) Distributed one-step upgraded estimation for non-uniformly and non-randomly distributed data, Computational Statistics & Data Analysis, 162:107265. |
2020 |
Ma, Y., Lan, W., Zhou, F., andWang, H., (2020) Approximate least squares estimation for spatial autoregressive models with covariates, Computational Statistics and Data Analysis, 143:106833. Liu, J., Ma, Y., andWang, H.（2020） Semiparametric model for covariance regression analysis, Computational Statistics and Data Analysis, 142:106815. Zhou, J., Li, D., Pan, R., andWang, H.（2020） Network GARCH model, Statistica Sinica, 30: 1723-1740. Zhu, X., Huang, D., Pan, R., andWang, H.（2020）Multivariate spatial autoregression for large scale social networks, Journal of Econometrics, 215(2): 591-606. Huang, D., Wang, F., Zhu, X., andWang, H.(2020) Two-mode network autoregressive model for large-scale networks, Journal of Econometrics, 216(1): 203-219. Ren, T., Wang, F. andWang, H.(2020) A Sequential Naive Bayes Method for Music Genre Classification Based on Transitional Information from Pitch and Beat, Statistics and Its Interface, 13(3): 361-371. Xu, K., Sun, L., Liu,J., Zhu, X., andWang, H.(2020) A spatial autoregression model with time-varying coefficients, Statistics and Its Interface, 13(2): 261-270. Ma, Y., Pan, R., Zou, T., andWang, H.(2020) A naive least squares method for spatial autoregression with covariates, Statistica Sinica, 30: 653—672. Zhang, X., Pan, R., Guan, G., Zhu, X., andWang, H.(2020) Logistic regression with network structure, Statistica Sinica, 30: 673—693. Sun, Z.,Wang, H.(2020) Network imputation for spatial autoregression model with incomplete Data, Statistica Sinica, 30(3)： 1419-1436. |
2019 |
Sun, L., Zheng, X., Jin, Y., Jiang, M., andWang, H(2019) Estimating promotion effects using big data: A partially profiled LASSO model with endogeneity correction, Decision Sciences, 50(4): 816-846. Zhu, X., Chang, X., Li, R., andWang, H.(2019) Portal nodes screening for large scale social networks, Journal of Econometrics, 209(2):145-157. Zhu, X., Wang, W.,Wang, H., and Wolfgang Karl Hardle （2019） Network quantile autoregression, Journal of Econometrics, 212(1): 345-358. Huang, D., Lan, W., Zhang, H., andWang, H.(2019) Least Squares Estimation of Spatial Autoregressive Models for Large-Scale Social Networks, Electronic Journal of Statistics,13(1): 1135-1165. Zhou, J., Zhou, J., Ding, Y., andWang,H.(2019) The magic of danmaku: A social interaction perspective of gift sending on live streaming platforms, Electronic Commerce Research and Applications, 34:100815. Gao, Z., Ma, Y.,Wang, H., and Yao, Q.(2019), Banded spatio-temporal autoregressions, Journal of Econometrics, 208(1): 211-230. Chen, Y., Pan, R., Guan, R., andWang, H.(2019), A case study for Beijing point of interest data using group linked Cox process, Statistics and Its Interface, 12: 331–344. Xu, K., Wang, J., Pan, R., andWang, H.(2019), Photographic diary: A new estimation approach to pm2.5 monitoring, Statistics and Its Interface, 12(3): 387–395. Chang, X., Huang, D., andWang, H.(2019), A popularity scaled latent space model for large-scale directed social network, Statistica Sinica, 29(3):1277-1299. |
2018 |
Lan, W., Fang, Z., Wang, H., and Tsai, C. L. (2018), Covariance matrix estimation via network structure, Journal of Business and Economics Statistics, 36(2): 359-369. Huang, M., Wang, S., Wang, H., and Jin, T. (2018), Maximum smoothed likelihood estimation for a class of semiparametric pareto mixture densities,Statistics and Its Interface, 11(1): 31-40. Xu, K., Sun, J., Liu, J., and Wang, H. (2018), An empirical investigation of taxi driver response behavior to ride-hailing requests: A spatio-temporal perspective, Plos One, 13(6): e0198605. Pan, R., Guan, R., Zhu, X., and Wang, H. (2018), A latent moving average model for network regression, Statistics and Its Interface, 11(4): 641-648. Huang, D., Guan, G., Zhou, J., and Wang, H. (2018), Network-based naive Bayes model for social network, Science China Mathematics, 61 (4): 627-640. Zhou, J., Huang, D., and Wang, H. (2018), A note on estimating network dependence in a discrete choice model, Statistics and its Interface, 11(3): 433-439. Huang, D., Chang, X., and Wang, H. (2018),Spatial autoregression with repeated measurements for social networks,Communications in Statistics - Theory and Methods, 47(15): 3715-3727. Huang, D., Zhou, J., and Wang, H. (2018), RFMS method for credit scoring based on bank card transaction data, Statistica Sinica, 28(4): 2903-2919. Cai, W., Guan, G., Pan, R., Zhu, X., and Wang, H. (2018), Network linear discriminant analysis, Computational Statistics & Data Analysis, 117: 32-44. Lan, W., Ma, Y., Zhao, J., Wang, H., and Tsai, C. L. (2018),Sequential model averaging for high dimensional linear regression models,Statistica Sinica, 28(1): 449-469. |
2017 |
Zhou, J., Huang, D., and Wang, H. (2017), A dynamic logistic regression for network link prediction, Science China Mathematics, 60(1): 165-176. Zhou, J., Tu, Y., Chen, Y., and Wang, H. (2017), Estimating spatial autocorrelation with sampled network data, Journal of Business & Economic Statistics, 35(1): 130-138. Zhu, X., Pan, R., Li, G., Liu, Y., Wang, H., et al. (2017), Network vector autoregression, The Annals of Statistics, 45(3): 1096-1123. Zou, T., Lan, W., Wang, H., and Tsai, C.L. (2017), Covariance regression analysis, Journal of the American Statistical Association, 112(517): 266-281. |
2016 |
Huang, D., Yin, J., Shi, T., and Wang, H. (2016), A statistical model for social network labeling, Journal of Business and Economics Statistics, 34(3): 368-374. Yan, T., Qin, H., and Wang, H., (2016), Asymptotics in undirected random graph models parameterized by the strengths of vertices, Statistica Sinica, 26(1): 273-293. Pan, R., Wang, H., and Li, R. (2016), Ultrahigh dimensional multi-class linear discriminant analysis by pairwise sure independence screening, Journal of the American Statistical Association, 111(513): 169-179. Lan, W., Zhong, P. S., Li, R., Wang, H., and Tsai, C. L. (2016), Testing a single regression coefficient in high dimensional model, Journal of Econometrics, 195(1): 154-168. |
2015 |
Zhu, X., Huang, D., Pan, R., and Wang, H. (2015), An EM algorithm for click fraud detection, Statistics and its Interface, 9(3): 389-394. Lu, X., Zhao, J., Chen, Y., and Wang, H. (2015), A choice model with a diverging choice set for POI data analysis, Statistics and its Interface, 9(3): 355-363. Pan, R. and Wang, H. (2015), A note on testing conditional independence for social network analysis, Science China: Mathematics, 58(6):1179-1190. Ma, Y., Lan, W., and Wang, H. (2015), Testing predictor significance with ultra high dimensional multivariate responses, Computational Statistics and Data Analysis, 83: 275-286. Lan, W., Luo, R., Tsai, C.L., Wang, H., and Yang, Y. (2015), Testing the diagonality of a large covariance matrix in a regression setting, Journal of Business and Economics Statistics, 33(1): 76-86. Ma, Y., Lan, W., and Wang, H. (2015), A high dimensional two sample test under a low dimensional structure, Journal of Multivariate Analysis, 140: 162-170. |
2014 |
Huang, D., Li, R., and Wang, H. (2014), Feature screening for ultrahigh dimensional categorical data with applications, Journal of Business and Economics Statistics, 32(2): 237-244. Huang, M., Li, R., Wang, H., and Yao, W. (2014), Estimating mixture of gaussian processes by kernel smoothing, Journal of Business and Economics Statistics, 32(2): 259-270. Lan, W., Wang, H., and Tsai, C.L. (2014), Testing covariates in high dimensional regression, Annals of Institute of Statistical Mathematics, 66(2): 279-301. Guan, G., Guo, J., and Wang, H. (2014), Varying naive Bayes models with application to classification of Chinese text documents, 32(3): 445-456. |
2013 |
Zhao, J., Leng, C., Li, L., and Wang, H. (2013), High dimensional influence measure, The Annals of Statistics, 41(5): 2639-2667. Zhang, Q., Li, D., and Wang, H. (2013), A note on tail dependence regression, Journal of Multivariate Analysis, 120: 163-172. Jiang, Q., Wang, H., Xia, Y., and Jiang, G. (2013), On a principal varying coefficient model, Journal of the American Statistical Association, 108(501): 228-236. An, B., Wang, H., and Guo, J. (2013), Testing the statistical significance of an ultra-high dimensional naive Bayes classifier, Statistics and Its Interface, 6(2): 223-229. An, B., Wang, H., and Guo, J. (2013), Multivariate regression shrinkage and selection by canonical correlation analysis, Computational Statistics and Data Analysis, 62: 93-107. |
2012 |
Wang, H. (2012), Factor profiled sure independence screening, Biometrika, 99(1): 15-28. Lan, W., Wang, H., and Tsai, C.L. (2012), A Bayesian information criterion for portfolio selection, Computational Statistics & Data Analysis, 56(1): 88-99. Liang, H., Wang, H., and Tsai, C.L. (2012), Profiled forward regression for ultrahigh dimensional variable screening in semiparametric partially linear models, Statistica Sinica, 22(2): 531-554. |
2011 |
Zhang, Q. and Wang, H. (2010), On BIC's selection consistency for discriminant analysis, Statistica Sinica, 21(2): 731-740. Pan, R., Wang, H., and Tsai, C.L. (2011), Regression analysis of asymmetric pairs in large scale network data. Communication in Statistics, 40(10): 1540-1547. Li, J., Pan, R., and Wang, H.(2011), Selection of best keywords: A Poisson regression model, Journal of Interactive Advertising, 11(1): 27-35. |
2010 |
Yin, J., Geng, Z., Li, R., and Wang, H. (2010), Nonparametric covariance model, Statistica Sinica, 20(1):469-479. Tsai, C. L., Wang, H., and Zhu, N. (2010), Does a Bayesian approach generate robust forecasts? evidence from applications in portfolio investment decisions, Annals of the Institute of Statistical Mathematics, 62(1): 109-116. Guan, Y. and Wang, H. (2010), Sufficient dimension reduction for spatial point processes directed by Gaussian random fields, Journal of Royal Statistical Society, Series B, 72(3): 367-387. Zhang, H. H., Lu, W., and Wang, H. (2010), On sparse estimation for semiparametric linear transformation models, Journal of Multivariate Analysis, 101(7): 1594-1606. |
2009 |
Wang, H. (2009), Forward regression for ultra-high dimensional variable screening, Journal of the American Statistical Association. 104(488): 1512-1524. Wang, H., Li, B., and Leng, C. (2009), Shrinkage tuning parameter selection with a diverging number of parameters, Journal of Royal Statistical Society, Series B, 71(3): 671-683. Leng, C. and Wang, H. (2009), On general adaptive sparse principal component analysis, Journal of Computational and Graphical Statistics, 18(1): 201-215. Wang, H. (2009), Rank reducible varying coefficient model, Journal of Statistical Planning and Inference, 139(3): 999-1011. Su, X., Tsai, C. L., Wang, H., Nickerson, D. M., and Li, B. (2009), Subgroup analysis via recursive partitioning, Journal of Machine Learning Research, 10: 141-158. Wang, H. and Xia, Y. (2009), Shrinkage estimation of the varying coefficient model, Journal of the American Statistical Association, 104(486): 747-757. Wang, H. and Tsai, C. L. (2009), Tail index regression, Journal of the American Statistical Association, 104(487): 1233-1240. Luo, R., Wang, H., and Tsai, C. L. (2009), Contour projected dimension reduction, The Annals of Statistics, 37(6): 3743-3778. Wang, H. and Tsai, C. L. (2009), A discussion on "model selection for generalized linear models with factor-augmented predictors", Applied Stochastic Models for Business and Industry, 25(3): 241-242. |
2008 |
Huang, D., Wang, H., and Yao, Q. (2008), Estimating GARCH models: when to use what? Econometrics Journal, 11(1): 27-38. Shao, J. and Wang, H. (2008), Confidence intervals based on survey data with nearest neighbor imputation, Statistica Sinica, 18(1): 281-297. Wang, H., Ni, L., and Tsai, C. L. (2008), Improving dimension reduction via contour-projection, Statistica Sinica, 18(1): 299-311. Wang, H. and Xia, Y. (2008), Sliced regression for dimension reduction, Journal of the American Statistical Association, 103(482): 811-821. Luo, R., Wang, H., and Tsai, C. L. (2008), On mixture regression shrinkage and selection via the Mr. Lasso, International Journal of Pure and Applied Mathematics, 46: 403-414. Wang, H. and Leng, C. (2008), A note on adaptive group lasso, Computational Statistics & Data Analysis, 52(12): 5277-5286. Jiang, G. and Wang, H. (2008), Should earnings thresholds be used as delisting criteria in stock market? Journal of Accounting and Public Policy, 27(5): 409-419. Luo, R. and Wang, H. (2008), A composite logistic regression approach for ordinal panel data regression, International Journal of Data Analysis Techniques and Strategies, 1(1): 29-43. Leng, C. and Wang, H. (2008), Tuning parameter selection consistency in an ultrahigh dimensional setup: a comment on the sure independence screening rule, Journal of Royal Statistical Society, Series B, 70: 896-897. |
2007 |
Wang, H. and Leng, C. (2007), Unified lasso estimation via least square approximation, Journal of American Statistical Association, 102(479): 1039-1048. Wang, H., Li, R., and Tsai, C. L (2007), Tuning parameter selectors for the smoothly clipped absolute deviation method, Biometrika, 94(3): 553-568. Wang, H., Li, G., and Tsai, C. L. (2007), Regression coefficients and autoregressive order shrinkage and selection via the lasso, Journal of Royal Statistical Society, Series B, 69(1): 63-78. Wang, H. (2007), A note on iterative marginal optimization: a simple algorithm for maximum rank correlation estimation, Computational Statistics and Data Analysis, 51(6): 2803-2812. Wang, H., Li, G., and Jiang, G. (2007), Robust regression shrinkage and consistent variable selection via the LAD-LASSO, Journal of Business & Economics Statistics, 25(7): 347-355. Wang, H. and Chow, S. C. (2007), Sample size calculation for comparing means, Encyclopedia of Clinical Trials, Wiley. DOI: 10.1002/9780471462422.eoct006. Wang, H. and Chow, S. C. (2007), Sample size calculation for comparing proportions, Encyclopedia of Clinical Trials, Wiley. DOI: 10.1002/9780471462422.eoct005. Wang, H. and Chow, S. C. (2007), Sample size calculation for comparing variabilities, Encyclopedia of Clinical Trials, Wiley. DOI: 10.1002/9780471462422.eoct008. Wang, H. and Chow, S. C. (2007), Sample size calculation for comparing time-to-event, Encyclopedia of Clinical Trials, Wiley DOI: 10.1002/9780471462422.eoct007. |
2005 |
Wang, H., Chow, S. C., and Chen, M. (2005), A Bayesian approach on sample size calculation for comparing means, Journal of Biopharmaceutical Statistics, 15(5): 799-807. |
2003 |
Chow, S.C., Shao, J., and Wang, H. (2003), Sample size calculations in clinical research, Chapman & Hall/CRC, New York, NJ. ISBN: 0824709705. Chow, S.C., Shao, J., and Wang, H. (2003), In vitro bioequivalence testing, Statistics in Medicine, 22(1): 55-68. Chow, S.C., Shao, J., and Wang, H. (2003), Statistical tests for population bioequivalence, Statistica Sinica, 13(2): 539-554. Wang, H. and Shao, J. (2003), Two-way contingency tables under conditional hot deck imputation, Statistica Sinica, 13(3): 613-623. Wang, H. and Chow, S.C. (2003), Imputation with item nonrespondents, Encyclopedia of Biopharmaceutical Statistics, 2nd Edition. Ed. Chow, S.C., Marcel Dekker, Inc., New York.443-448. Wang, H., Zhang, Y., Shao, J., and Chow, S.C. (2003), In vitro bioequivalence testing, Encyclopedia of Biopharmaceutical Statistics, 2nd Edition. Ed. Chow, S.C., Marcel Dekker, Inc. New York. 449-455. Wang, H. (2003), Imputation in clinical research, Encyclopedia of Biopharmaceutical Statistics, 2nd Edition. Ed. Chow, S. C., Marcel Dekker, Inc. New York, New York. 437-442. Wang, H., Cheng, B., and Chow, S.C. (2003), Sample size determination based on rank tests in clinical research, Journal of Biopharmaceutical Statistics, 13(4): 735-51. Lee, Y., Wang, H., and Chow, S.C. (2003), Comparing variabilities in clinical research, Encyclopedia of Biopharmaceutical Statistics, 2nd Edition. Ed. Chow, S.C., Marcel Dekker, Inc. New York. 214-230. |
2002 |
Shao, J. and Wang, H. (2002), Sample correlation coefficients based on survey data under regression imputation, Journal of American Statistical Association, 97(458): 544-552. Wang, H. and Chow, S.C. (2002), A practical approaches for parallel trials without equal variance assumption, Statistics in Medicine, 21(20): 3137-3151. Chow, S.C., Shao, J., and Wang, H. (2002), Individual bioequivalence testing under 2x3 designs, Statistics in Medicine, 21(5): 629-648. Chow, S.C., Shao, J., and Wang, H. (2002), Probability lower bound for USP/NF test, Journal of Biopharmaceutical Statistics, 12(1): 79-92. Wang, H. and Chow, S.C. (2002), On statistical power for average bioequivalence testing under replicated crossover design, Journal of Biopharmaceutical Statistics, 12(3): 295-309. Chow, S.C., Shao, J., and Wang, H. (2002), A note on sample size calculation for mean comparisons based on non-central t-statistics, Journal of Biopharmaceutical Statistics, 12(4): 441-456. Wang, H., Chow, S.C., and Li, G. (2002), On sample size calculation based on odds ratio in clinical trials, Journal of Biopharmaceutical Statistics, 12(4): 471-483. Lee, Y., Shao, J., Chow, S.C., and Wang, H. (2002), Test for inter-subject and total variabilities under crossover design, Journal of Biopharmaceutical Statistics, 12(4): 503-534. |
2001 |
Chow, S.C. and Wang, H. (2001), On sample size calculation in bioequivalence trials, Journal of Pharmacokinetics and Pharmacodynamics, 28(2): 155-169. Wang, H. (2001), Two-way contingency tables with conditionally and marginally imputed data, Ph.D. thesis, University of Wisconsin-Madison. |
中文发表 |
2023 |
韩苗、许可、伍书缘、王汉生，“基于自适应背景差分与深度学习的矿山巷道不安全行为的自动识别”，《经济管理学刊》，已接收。 肖筱林、王汉生，“大数据分析在宏观金融领域的文献综述--基于中央银行的视角”，《经济管理学刊》，已接收。 温中卉，王汉生，“高分辨率图像分类方法在直播汽车车型识别场景的应用”，《经济管理学刊》，已接收。 |
2021 |
黄丹阳，朱映秋，南金伶，王汉生，“基于交易流水的信用卡套现交易及商户识别”，《数理统计与管理》，已接收。 |
2019 |
周静，沈俏蔚，涂平，王汉生（2019），“社交网络中用户关注类型与发帖类型对发帖行为的影响”，《管理科学》，第32卷第2期，第67-76页。 |
2018 |
王汉生，“朴素的数据价值观 ”《新经济导刊》，2018年6月号 总第265期 周静，沈俏蔚，涂平，王汉生，“原创还是转发？基于社交媒体UGC的交互效用研究”，《营销科学学报》，第13卷第4辑，第55-67页。 |
2017 |
周静，周小宇，王汉生（2017），“自我网络特征对电信客户流失的影响研究”，《管理科学》，第30卷第5期，第28-37页。 |
2015 |
林野，周静，王汉生（2015）“电视广告定价与社交收视率”，《统计学评论》，第9卷，第93-103页。 |
2014 |
王汉生（2014）“大数据概念被神化”，《新京报》，2014-06-27。 王汉生（2014）“从数据到价值”，《彭博商业周刊：中文版》。 |
2013 |
李季，周李超，王汉生（2013） “多产品协同促销模式下的零售商促销时间决策模型”，《中国管理科学》，第21（4）期，第89—97页。 |
2010 |
王汉生、张瀚宇、何天英、郭露茜（2010） “上市公司财务参数与其股价波动性关系探讨”，《证券市场导报》，总第211期，第74—77页。 姜国华，王汉生（2010） “取消ST制度，完善退市制度，促进股市健康发展”，《证券市场导报》，2010年，6月增刊，第42—48页。 黄达、王汉生（2010） “GARCH模型估计方法选择及对上证指数的应用”，《数理统计与管理》，第29卷，第3期，第544—549页。 |
2009 |
胡新杰、罗荣华、江明华、王汉生（2009） “基于高维0-1变量的EM 算法在移动通信客户细分中的应用”，《数理统计与管理》，2009，第28卷 第2期，第264—269页。 |
2008 |
岳衡、王汉生、姜国华（2008） “大股东资金占用与上市公司ST关系的研究”，《金融学季刊》，第4卷，第2期，第1—19页。 李季、王汉生、涂平（2008） “对于尝试-重购新产品扩散模型的改进：logit模型及NILS估计”，《中国管理科学》，第16卷，第6期，2008年12月，，第105—111页。 |
2007 |
丁嘉丽、符国群、涂平、王汉生（2007） “消费者对不同强弱品牌的质量感知：评价模式的调节作用”，《数理统计与管理》，第26卷，第6期， 第977—983页。 姜国华、王汉生（2007） “ST：不怕烂货就怕假货”，《新财经》，2007年6月，第90页。 常莹、李季、王汉生、涂平（2007） “具备重复购买机制的新产品扩散模型：理论模型与非线性最小一乘估计”， 《营销科学学报》，第2卷，第4辑，第22—31页。 |
2006 |
姜国华、王汉生（2006） “审计作为证券市场有效监管工具之探讨”， 《中国注册会计师》，理论研究版，2006年10月号，第63—65页。 王汉生、江明华、曹丽娜、金英（2006） “超级市场零售商品的购物篮分析”， 《营销科学学报》，第2卷，第1辑，第71—77页。 胡健颖、姜国华、王汉生（2006） “实证研究中预测模型的选择：从逐步回归到信息标准”，《数理统计与管理》， 2006年1月，第25卷，第1期，第21—26页。 |
2005 |
姜国华、王汉生（2005） “上市公司两年亏损就应该被ST吗？”，《经济研究》，2005年，第3期，第100—107页。 王汉生、胡健颖（2005） “山西省商品房市场的发展规律”，《数理统计与管理》，第24卷，第2期，第7—13页。 王汉生、江明华、陈可（2005） “增加新功能产品的非线性定价研究”，《营销科学学报》，第1卷，第2辑，第132—137页。 |
2004 |
姜国华、王汉生（2004） “财物报表分析与上市公司ST预测研究”，《审计研究》，2004年，第6期，第60—63页。 |
