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. Hailun Wang, Pak Sham, Tiejun Tong and Herbert Pang (2020)
    Pathway-based single-cell RNA-Seq classiļ¬cation and construction of co-occurrence network using random forests
    IEEE Journal of Biomedical and Health Informatics, in press.

  2. 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.

  3. 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.

  4. 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]

  5. 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.

  6. 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.

  7. 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.

  8. 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.

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

  10. 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.

  11. 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.

  12. 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]

  13. 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.

  14. 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.

  15. 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.

  16. 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.

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

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

  19. 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.

  20. 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.

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

  22. 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.

  23. 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.

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

  25. 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]

  26. 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.

  27. 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.