Publications in high-dimensional data analysis

-- * denotes students under my supervision.
-- The research in this area has been supported by the General Research Fund (GRF) with Grant Nos. HKBU12303918 and HKBU202711.

  1. Zongliang Hu*, Zhishui Hu, Kai Dong*, Tiejun Tong and Yuedong Wang (2020+)
    A shrinkage approach to joint estimation of multiple covariance matrices
    Metrika, in press.

  2. 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, 24: 1814-1822.

  3. 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.

  4. 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.

  5. 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]

  6. Yujie Li, Gaorong Li and Tiejun Tong (2017)
    Sequential profile Lasso for ultra-high dimensional partially linear models
    Statistical Theory and Related Fields, 1: 234-245.

  7. 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.

  8. 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.

  9. 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.

  10. Kai Dong*, Hongyu Zhao, Tiejun Tong and Xiang Wan (2016)
    NBLDA: Negative binomial linear discriminant analysis for RNA-Seq data
    BMC Bioinformatics, 17: 369.

  11. 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.

  12. 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.

  13. 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]

  14. 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.

  15. 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.

  16. 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.

  17. Tiejun Tong, Zeny Feng, Julia S. Hilton* and Hongyu Zhao (2013)
    Estimating the proportion of true null hypotheses using the pattern of observed p-values
    Journal of Applied Statistics, 40: 1949-1964.

  18. Tiejun Tong, Liang Chen and Hongyu Zhao (2012)
    Improved mean estimation and its application to diagonal discriminant analysis
    Bioinformatics, 28: 531-537.

  19. Tiejun Tong, Homin Jang and Yuedong Wang (2012)
    James-Stein type estimators of variances
    Journal of Multivariate Analysis, 107: 232-243.

  20. 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.

  21. Hyunmin Kim, Jihye Kim, Heather Selby, Dexiang Gao, Tiejun Tong, Tzu Lip Phang and Aik Choon Tan (2011)
    A short survey of computational analysis methods in analysing ChIP-seq data
    Human Genomics, 5: 117-123.

  22. Song Huang, Tiejun Tong and Hongyu Zhao (2010)
    Bias-corrected diagonal discriminant rules for high-dimensional classification
    Biometrics, 66: 1096-1106.

  23. Dexiang Gao, Jihye Kim, Hyunmin Kim, Tzu Phang, Heather Selby, AikChoon Tan and Tiejun Tong (2010)
    A survey of statistical software for analysing RNA-seq data
    Human Genomics, 5: 56-60.

  24. Herbert Pang, Keita Ebisu, Emi Watanabe, Laura Sue and Tiejun Tong (2010)
    Analyzing breast cancer microarrays of African Americans using shrinkage-based discriminant analysis
    Human Genomics, 5: 5-16.

  25. Herbert Pang, Tiejun Tong and Hongyu Zhao (2009)
    Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data
    Biometrics, 65: 1021-1029.

  26. 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]

  27. 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.

  28. 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.