Research

Manuscripts:

  • Wang* T: Graph-assisted inverse regression for count data and its application to next generation sequencing data analysis. Journal of Computational and Graphical Statistics (under review) 2019
  • Song Y, Zhao H, Wang* T: An adaptive independence test for microbiome community data. Biometrics (under review) 2019

2019:

  • Wang* T, Yang C, Zhao H: Prediction analysis of microbiome sequencing data. Biometrics. doi:10.1111/biom.13061
  • Xu P, Wang* T: On supervised reduction and its dual. Statistica Sinica. doi:10.5705/ss.202017.0532
  • Sun Q, Zhu R, Wang T, Zeng D: Counting process based dimension reduction methods for censored outcomes. Biometrika 2019, 106(1): 181–196. (R package orthoDr)

2018:

  • Kaufman J, Montalvo-Ortiz J, Holbrook H, O’Loughlin K, Orr C, Kearney C, Yang B, Wang T, Zhao H, Althoff R, Garavan H, Gelernter J, Hudziak J: Adverse childhood experiences, epigenetic measures, and obesity in youth. The Journal of Pediatrics 2018, 202:150-156
  • Cui P, Zhong T, Wang Z, Wang T, Zhao H, Liu C, Lu H: Identification of human circadian genes based on time course gene expression profiles by using a deep learning method. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 2018, 1864(6):2274-2283
  • Wang T, Zhu L: Flexible dimension reduction in regression. Statistica Sinica 2018, 28:1009-1029
  • Wang T, Chen M, Zhao H, Zhu L: Estimating a sparse reduction for general regression in high dimensions. Statistics and Computing 2018, 28(1): 33-46

2017:

  • Wang T, Zhao H: Constructing predictive microbial signatures at multiple taxonomic levels. Journal of the American Statistical Association 2017, 112(519):1022-1031
  • Wang T*, Zhao H: A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms. Biometrics 2017, 73(3):792-801 (Amongst articles published between 2016-2017, this article was one of the most frequently downloaded in the 12 months after online publication; amongst articles published between January 2017 and December 2018, this paper received some of the most downloads in the 12 months following online publication)
  • Lin Z, Wang T, Yang C, Zhao H: On joint estimation of Gaussian graphical models for spatial and temporal data. Biometrics 2017, 73(3):769-779
  • Wang T, Zhao H: Structured subcomposition selection in regression and its application to microbiome data analysis. The Annals of Applied Statistics 2017, 11(2):771-791
  • Zhu X, Wang T, Zhao J, Zhu L: Inference for biased transformation models. Computational Statistics and Data Analysis 2017, 109:105-120
  • Wang T, Wen X, Zhu L: Multiple-population shrinkage estimation via sliced inverse regression. Statistics and Computing 2017, 27:103-114

2016:

  • Liu C, Jiang J, Gu J, Yu Z, Wang T*, Lu H: High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI). BMC Systems Biology 2016, 10(4):118
  • Guo X, Wang T, Zhu L: Model checking for parametric single-index models: a dimension reduction model-adaptive approach. Journal of the Royal Statistical Society: Series B 2016, 78(5):1013-1035
  • Xu P, Zhang J, Huang X, Wang T: Efficient estimation for marginal generalized partially linear single-index models with longitudinal data. Test 2016, 25(3):413-431
  • Wang T, Chen M, Zhao H: Estimating DNA methylation levels by joint modeling of multiple methylation profiles from microarray data. Biometrics 2016, 72(2):354-363

2015:

  • Wang T, Zhang J, Liang H, Zhu L: Estimation of a groupwise additive multiple-index model and its applications. Statistica Sinica 2015, 25(2):551-567
  • Xu P, Wang T, Zhu H, Zhu L: Double penalized H-likelihood for selection of fixed and random effects in mixed effects models. Statistics in Biosciences 2015, 7:108-128
  • Wang T, Xu P, Zhu L: Variable selection and estimation for semi-parametric multiple-index models. Bernoulli 2015, 21:242-275
  • Wang T, Zhu L: A distribution-based LASSO for a general single-index model. Science China Mathematics 2015, 58:109-130

2014:

  • Wang T, Guo X, Zhu L, Xu P: Transformed sufficient dimension reduction. Biometrika 2014, 101(4):815-829
  • Feng Z, Wang T, Zhu L: Transformation-based estimation. Computational Statistics and Data Analysis 2014, 78:186-205
  • Niu C, Guo X, Wang T, Xu P: Regret theory and the competitive firm: A comment. Economic Modelling 2014, 41:312-315
  • Guo X, Wang T, Xu W, Zhu L: Dimension reduction with missing response at random. Computational Statistics and Data Analysis 2014, 69:228-242

2013:

  • Wang T, Xu P, Zhu L: Penalized minimum average variance estimation. Statistica Sinica 2013, 23(2):543-569
  • Wang T, Zhu L: Sparse sufficient dimension reduction using optimal scoring. Computational Statistics and Data Analysis 2013, 57:223-232

2012:

  • Zhang J, Wang T, Zhu L, Liang H: A dimension reduction based approach for estimation and variable selection in partially linear single-index models with high-dimensional covariates. Electronic Journal of Statistics 2012, 6:2235-2273
  • Wang T, Xu P, Zhu L: Non-convex penalized estimation in high-dimensional models with single-index structure. Journal of Multivariate Analysis 2012, 109:221-235
  • Zhu L, Wang T, Zhu L: Sufficient dimension reduction in regressions with missing predictors. Statistica Sinica 2012, 22(4):1611-1637

2011:

  • Wang T, Zhu L: Consistent tuning parameter selection in high dimensional sparse linear regression. Journal of Multivariate Analysis 2011, 102(7):1141-1151

2010:

  • Zhu L, Wang T, Zhu L, Ferré L: Sufficient dimension reduction through discretization-expectation estimation. Biometrika 2010, 97(2):295-304