![]()
-
副教授
- 电子邮箱：0cfde6a93bebfbf93a81f2836b5cbc62564ca52797af4e34099290e0e3e130a5d3eb11f5a9cbc81dcbe6c5dc03b92898ed32bd59b2a1a20f55c4e3da2756dd12a15fe36c60375866888a72951ce50a49f96a477d9705c6ed72c9c6e4726eb9caf5e02692726e729b9cf3ca04306ff6bfa034d10bceaf388d66adf9626c068b53
- 联系方式：+86-551-63600560
- 学位：博士
访问量：
-
[31]Active learning in multiple-class classification problems via individualized binary models, Computational Statistics and Data Analysis, 2020, 145.
-
[32]A general robust t-process regression model, Computational Statistics and Data Analysis, 2021, 154.
-
[33]Nonparametric random effects functional regression model using Gaussian process priors, Statistica Sinica, 2021, 31(1): 53-78.
-
[34]Sequential adaptive variables and subject selection for GEE methods, Biometrics, 2020, 76(2): 496-507.
-
[35]Incorporating graphical structure of predictors in sparse quantile regression, Journal of Business & Economic Statistics, 2020, 39(3): 783-792.
-
[36] Liu, X.,Wang, Z. and Wu, Y. (2013). Group variable selection and estimation in the tobit censored response model, Computational Statistics and Data Analysis, 60, 80-89..
-
[37] Wang, Z., Tsai, C. and Chang, Y. (2012). Identifying differential gene sets through the linear combination of gene sets that maximizes the area under the receiver operating characteristic curve. Journal of Proteomics & Bioinformatics, 5, 073-083..
-
[38] Wang, Z. and Chang, Y. (2011). Markers selection via maximizing the partial area under the ROC curve of linear risk scores. Biostatistics. 12, 369-385..
-
[39] Wang, Z.,Wu, Y. and Zhao, L. (2010). A Lasso-type approach to variable selection and estimation for censored regression model. Chinese Journal of Applied Probability and Statistics, 26, 66-80..
-
[40]Feng, Y., Ma, W.,Wang, Z., Ying, Z and Yang, Y. (2009). Alignment of protein mass spectrometry data by integrated Markov chain shifting method. Statistics and Its Interface, 2, 329-340..
