(* denotes students under my supervision)
Hailun Wang, Pak Sham, Tiejun Tong and Herbert Pang (2020)
Pathway-based single-cell RNA-Seq classiﬁcation and construction of co-occurrence network using random forests
IEEE Journal of Biomedical and Health Informatics, in press.
Sanying Feng, Gaorong Li, Heng Peng and Tiejun Tong (2020)
Varying coefficient panel data model with interactive fixed effects
Statistica Sinica, in press.
Fengyang He, Huixia Judy Wang and Tiejun Tong (2020)
Extremal linear quantile regression with Weibull-type tails
Statistica Sinica, in press.
Shuwei Li, Tao Hu, Tiejun Tong and Jianguo Sun (2020)
Semi-parametric regression analysis of multivariate doubly-censored data
Statistical Modelling, in press.
Hong Zhang, Tiejun Tong, John Landers and Zheyang Wu (2020)
TFisher: A powerful truncation and weighting procedure for combining p-values
Annals of Applied Statistics, 14: 178-201.
Yuejin Zhou, Sze-Yui Ho*, Jiahua Liu* and Tiejun Tong (2020)
Hypothesis testing for normal distributions: a unified framework and new developments
Statistics and Its Interface, 13: 167-179.
Wenwu Wang, Ping Yu, Lu Lin and Tiejun Tong (2019)
Robust estimation of derivatives using locally weighted least absolute deviation regression
Journal of Machine Learning Research, 20(60): 1−49.
Zongliang Hu*, Tiejun Tong and Marc G. Genton (2019)
Diagonal likelihood ratio test for equality of mean vectors in high-dimensional data
Biometrics, 75: 256-267.
Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin and Jun Zhang (2019)
A statistical normalization method and differential expression analysis for RNA-seq data between different species
BMC Bioinformatics, 20: 163.
Yiping Yang, Tiejun Tong and Gaorong Li (2019)
SIMEX estimation for single-index model with covariate measurement error
AStA Advances in Statistical Analysis, 103: 137-161.
-- The paper has been recognized as a highly cited paper in ISI Web of Science.
Dehui Luo*, Xiang Wan, Jiming Liu and Tiejun Tong (2018)
Optimally estimating the sample mean from the sample size, median, mid-range and/or mid-quartile range
Statistical Methods in Medical Research, 27: 1785-1805.
[Excel Worksheet for Optimal Mean Estimation]
-- This paper establishes a new "rule of thumb" between the sample mean, median and mid-range.
The paper has been recognized as a highly cited paper and a hot paper in ISI Web of Science, with a total of 100 citations (and 148 citations in Google Scholar) as of 12/03/2020, including 10 citations in 2018, 73 citations in 2019, and 17 citations in 2020.
Yan Zhou, Xiang Wan, Baoxue Zhang and Tiejun Tong (2018)
Classifying next-generation sequencing data using a zero-inflated Poisson model
Bioinformatics, 34: 1329-1335. [R package for ZIPLDA]
Yuejin Zhou, Yebin Cheng, Wenlin Dai* and Tiejun Tong (2018)
Optimal difference-based estimation for partially linear models
Computational Statistics, 33: 863-885.
Wenlin Dai*, Tiejun Tong and Lixing Zhu (2017)
On the choice of difference sequence in a unified framework for variance estimation in nonparametric regression
Statistical Science, 32: 455-468.
-- As quoted from the abstract of the paper, the main contributions of this paper are as follows:
"The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades."
Zongliang Hu*, Kai Dong*, Wenlin Dai* and Tiejun Tong (2017)
A comparison of methods for estimating the determinant of high-dimensional covariance matrix
International Journal of Biostatistics, 13: 20170013.
Yan Zhou, Baoxue Zhang, Gaorong Li, Tiejun Tong and Xiang Wan (2017)
GD-RDA: A new regularized discriminant analysis for high dimensional data
Journal of Computational Biology, 24: 1099-1111.
Yujie Li, Gaorong Li, Heng Lian and Tiejun Tong (2017)
Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models
Journal of Multivariate Analysis, 155: 133-150.
Wenlin Dai*, Tiejun Tong and Marc G. Genton (2016)
Optimal estimation of derivatives in nonparametric regression
Journal of Machine Learning Research, 17(164): 1-25.
Kai Dong*, Hongyu Zhao, Tiejun Tong and Xiang Wan (2016)
NBLDA: Negative binomial linear discriminant analysis for RNA-Seq data
BMC Bioinformatics, 17: 369.
Jiacheng Yuan, Herbert Pang, Tiejun Tong, Dong Xia, Wenzhao Guo and Peter Mesenbrink (2016)
Seamless phase IIa/IIb and enhanced dose finding adaptive design
Journal of Biopharmaceutical Statistics, 26: 912-923.
Kai Dong*, Herbert Pang, Tiejun Tong and Marc G. Genton (2016)
Shrinkage-based diagonal Hotelling tests for high-dimensional small sample size data
Journal of Multivariate Analysis, 143: 127-142.
Yebin Cheng, Dexiang Gao and Tiejun Tong (2015)
Bias and variance reduction in estimating the proportion of true null hypotheses
Biostatistics, 16: 189-204. [R codes]
Yuejin Zhou, Yebin Cheng, Lie Wang and Tiejun Tong (2015)
Optimal difference-based variance estimation in heteroscedastic nonparametric regression
Statistica Sinica, 25: 1377-1397.
Cheng Wang, Guangming Pan, Tiejun Tong and Lixing Zhu (2015)
Shrinkage estimation of large dimensional precision matrix using random matrix theory
Statistica Sinica, 25: 993-1008.
Yiping Yang, Gaorong Li and Tiejun Tong (2015)
Corrected empirical likelihood for a class of generalized linear measurement error models
Science China Mathematics, 58: 1523-1536.
Wenlin Dai*, Yanyuan Ma, Tiejun Tong and Lixing Zhu (2015)
Difference-based variance estimation in nonparametric regression with repeated measurement data
Journal of Statistical Planning and Inference, 163: 1-20.
Xiang Wan, Wenqian Wang*, Jiming Liu and Tiejun Tong (2014)
Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range
BMC Medical Research Methodology, 14: 135.
[Excel Worksheet for Standard Deviation Estimation]: This worksheet works for Excel 2013 or newer versions.
For Excel 2010 or earlier versions, please modify the inverse normal function "NORM.INV" as "NORMINV".
-- This paper establishes an improved "rule of thumb" between the sample range and the sample standard deviation.
The paper has been recognized as a highly cited paper (and a hot paper in 2016) in ISI Web of Science, with a total of 900 citations (and 1152 citations in Google Scholar) as of 15/04/2020, including 5 citations in 2015, 55 citations in 2016, 135 citations in 2017, 214 citations in 2018, 395 citations in 2019, and 96 citations in 2020.
Gaorong Li, Heng Peng, Kai Dong* and Tiejun Tong (2014)
Simultaneous confidence bands and hypothesis testing for single-index models
Statistica Sinica, 24: 937-955.
Cheng Wang, Tiejun Tong, Longbing Cao and Baiqi Miao (2014)
Nonparametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices
Journal of Multivariate Analysis, 125: 222-232.
Tiejun Tong, Cheng Wang and Yuedong Wang (2014)
Estimation of variances and covariances for high-dimensional data: a selective review
Wiley Interdisciplinary Reviews: Computational Statistics, 6: 255-264.
Tiejun Tong, Yanyuan Ma and Yuedong Wang (2013)
Optimal variance estimation without estimating the mean function
Bernoulli, 19: 1839-1854.
Herbert Pang, Tiejun Tong and Michael K. Ng (2013)
Block-diagonal discriminant analysis and its bias-corrected rules
Statistical Applications in Genetics and Molecular Biology, 12: 347-359.
Gaorong Li, Heng Peng and Tiejun Tong (2013)
Simultaneous confidence bands for nonparametric fixed effects panel data models
Economics Letters, 119: 229-232.
Tiejun Tong, Liang Chen and Hongyu Zhao (2012)
Improved mean estimation and its application to diagonal discriminant analysis
Bioinformatics, 28: 531-537.
Tiejun Tong, Homin Jang and Yuedong Wang (2012)
James-Stein type estimators of variances
Journal of Multivariate Analysis, 107: 232-243.
Herbert Pang, Stephen George, Ken Hui and Tiejun Tong (2012)
Gene selection using iterative feature elimination random survival forests
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9, 1422-1431.
Liqian Peng* and Tiejun Tong (2011)
A note on two-sample t test with one variance unknown
Statistical Methodology, 8: 528-534.
Song Huang, Tiejun Tong and Hongyu Zhao (2010)
Bias-corrected diagonal discriminant rules for high-dimensional classification
Biometrics, 66: 1096-1106.
Nairanjana Dasgupta, Eleanne Solorzano and Tiejun Tong (2010)
Comparing multiple treatments to both positive and negative controls
Journal of Statistical Planning and Inference, 140: 180-188.
Herbert Pang, Tiejun Tong and Hongyu Zhao (2009)
Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data
Biometrics, 65: 1021-1029.
Tiejun Tong and Hongyu Zhao (2008)
Practical guidelines for assessing power and false discovery rate for a fixed sample size in microarray experiments
Statistics in Medicine, 27: 1960-1972. [R codes]
Liang Chen, Tiejun Tong and Hongyu Zhao (2008)
Considering dependence among genes and markers for false discovery control in eQTL mapping
Bioinformatics, 24: 2015-2022.
Tiejun Tong and Yuedong Wang (2007)
Optimal shrinkage estimation of variances with applications to microarray data analysis
Journal of the American Statistical Association, 102: 113-122.
Anna Liu, Tiejun Tong and Yuedong Wang (2007)
Smoothing spline estimation of variance function
Journal of Computational and Graphical Statistics, 16: 312-329.
Tiejun Tong and Yuedong Wang (2005)
Estimating residual variance in nonparametric regression using least squares
Biometrika, 92: 821-830.
Chun Su and Tiejun Tong (2004)
Almost sure convergence of the general Jamison weighted B-valued random variables
Acta Mathematica Sinica, English Series, 20: 181-192.