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Tiejun Tong
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2020 – today
- 2022
- [j20]Yanyan Ouyang, Jiamin Liu, Tiejun Tong, Wangli Xu:
A rank-based high-dimensional test for equality of mean vectors. Comput. Stat. Data Anal. 173: 107495 (2022) - [j19]Hongmei Lin, Jianhong Shi, Tiejun Tong, Riquan Zhang:
Partially Linear Single-Index Model in the Presence of Measurement Error. J. Syst. Sci. Complex. 35(6): 2361-2380 (2022) - [j18]Wenwu Wang, Jun Lu, Tiejun Tong, Zhonghua Liu:
Debiased learning and forecasting of first derivative. Knowl. Based Syst. 236: 107781 (2022) - [c1]Wanjun Ning, Zimu Xu, Jingjin Wu, Tiejun Tong:
Sequence Q-Learning Algorithm for Optimal Mobility-Aware User Association. ICC 2022: 726-732 - 2020
- [j17]Hailun Wang, Pak Sham, Tiejun Tong, Herbert Pang:
Pathway-Based Single-Cell RNA-Seq Classification, Clustering, and Construction of Gene-Gene Interactions Networks Using Random Forests. IEEE J. Biomed. Health Informatics 24(6): 1814-1822 (2020)
2010 – 2019
- 2019
- [j16]Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang:
A statistical normalization method and differential expression analysis for RNA-seq data between different species. BMC Bioinform. 20(1): 163:1-163:10 (2019) - [j15]Wenwu Wang, Ping Yu, Lu Lin, Tiejun Tong:
Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression. J. Mach. Learn. Res. 20: 60:1-60:49 (2019) - 2018
- [j14]Yan Zhou, Xiang Wan, Baoxue Zhang, Tiejun Tong:
Classifying next-generation sequencing data using a zero-inflated Poisson model. Bioinform. 34(8): 1329-1335 (2018) - [j13]Yuejin Zhou, Yebin Cheng, Wenlin Dai, Tiejun Tong:
Optimal difference-based estimation for partially linear models. Comput. Stat. 33(2): 863-885 (2018) - 2017
- [j12]Yan Zhou, Baoxue Zhang, Gaorong Li, Tiejun Tong, Xiang Wan:
GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data. J. Comput. Biol. 24(11): 1099-1111 (2017) - [j11]Yujie Li, Gaorong Li, Heng Lian, Tiejun Tong:
Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models. J. Multivar. Anal. 155: 133-150 (2017) - 2016
- [j10]Kai Dong, Hongyu Zhao, Tiejun Tong, Xiang Wan:
NBLDA: negative binomial linear discriminant analysis for RNA-Seq data. BMC Bioinform. 17: 369 (2016) - [j9]Wenlin Dai, Tiejun Tong, Marc G. Genton:
Optimal Estimation of Derivatives in Nonparametric Regression. J. Mach. Learn. Res. 17: 164:1-164:25 (2016) - [j8]Kai Dong, Herbert Pang, Tiejun Tong, Marc G. Genton:
Shrinkage-based diagonal Hotelling's tests for high-dimensional small sample size data. J. Multivar. Anal. 143: 127-142 (2016) - 2014
- [j7]Yuejin Zhou, Yebin Cheng, Tiejun Tong:
A Least Squares Method for Variance Estimation in Heteroscedastic Nonparametric Regression. J. Appl. Math. 2014: 585146:1-585146:14 (2014) - [j6]Cheng Wang, Tiejun Tong, Longbing Cao, Baiqi Miao:
Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices. J. Multivar. Anal. 125: 222-232 (2014) - [j5]Xiang Wan, Jiming Liu, William K. Cheung, Tiejun Tong:
Learning to improve medical decision making from imbalanced data without a priori cost. BMC Medical Informatics Decis. Mak. 14: 111 (2014) - 2012
- [j4]Tiejun Tong, Liang Chen, Hongyu Zhao:
Improved mean estimation and its application to diagonal discriminant analysis. Bioinform. 28(4): 531-537 (2012) - [j3]Tiejun Tong, Homin Jang, Yuedong Wang:
James-Stein type estimators of variances. J. Multivar. Anal. 107: 232-243 (2012) - [j2]Herbert Pang, Stephen L. George, Ken Hui, Tiejun Tong:
Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes. IEEE ACM Trans. Comput. Biol. Bioinform. 9(5): 1422-1431 (2012)
2000 – 2009
- 2008
- [j1]Liang Chen, Tiejun Tong, Hongyu Zhao:
Considering dependence among genes and markers for false discovery control in eQTL mapping. Bioinform. 24(18): 2015-2022 (2008)
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