Selected Publications

  • Yang, Y., Wang, C., Liu, L., Buxbaum, J., He, Z., & Ionita-Laza, I. (2022). KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design. The American Journal of Human Genetics, 109(10), 1761-1776.
    [ PDF link: ]
  • Li, Z., Xu, J., Zhou, W., and Zhao, N. (2022). Penalized Jackknife Empirical Likelihood in High Dimensions. Statistica Sinica. In press.
  • Yang, Yi. , Basu, Saonli. & Zhang, Lin. (Feb 2022). A Bayesian hierarchically structured prior for gene-based association test with multiple traits in genome-wide association studies. Genetic epidemiology. 46 (1). 63 - 72. doi:10.1002/gepi.22437
    [ PDF link: ]
  • Huang, T.-J., Luedtke, A. and McKeague, I. W. (2022)
    Efficient Estimation of the Maximal Association between Multiple Predictors and a Survival Outcome.
    Submitted to the Annals of Statistics [arXiv link]
  • Lee, C., Wong, K., Lam, K., and Xu, J. (2021). Analysis of Clustered Interval-Censored Data using a Class of Semiparametric Partly Linear Frailty Transformation Models. Biometrics. In press.
  • Wang, W., Xu, J., Schwartz, J., Baccarelli, A., and Liu, Z. (2021). Causal mediation analysis with latent subgroups. Statistics in Medicine, 40, 5628-5641.
  • Zhou, Y., Zhang, L., Xu, J., Zhang, J., and Yan, X. (2021). Category encoding method to select feature genes for the classification of bulk and single-cell RNA-seq data. Statistics in Medicine, 40, 4077-4089.
  • Liu, Y., Xu, J., and Li, G. (2021). Sure Joint Feature Screening in Nonparametric Transformation Model with Right Censored Data. Canadian Journal of Statistics, 49, 549-565.
  • Wong, T., Wong, C., Zhang, X., Zhou, Y., Xu, J., Yuen, K., Wan, J., and Louie, J. (2021). The Association Between Coffee Consumption and Metabolic Syndrome in Adults: A Systematic Review and Meta-Analysis. Advances in Nutrition, 12(3):708-721.
  • Yang, Yi. , Basu, Saonli. & Zhang, Lin. (Jun 2021). A Bayesian hierarchically structured prior for rare‐variant association testing. Genetic epidemiology. 45 (4). 413 - 424. doi:10.1002/gepi.22379
    [ PDF link: ]
  • McKeague, I. W. and Swan, Y. (2021)
    Stein's Method and Approximaing the Multidimensional Quantum Harmonic Oscillator
    [arXiv link]
  • McKeague, I. W.(2021)
    Non-Commutative Probability and Multiplicative Cascades
    Statistical Science, 36, 256-263.[pdf]
  • Chang, H.-W.,McKeague, I. W. and Wang, Y.-J. (2021)
    A Case Study of Non-inferiority Testing with Survival Outcomes
    Case Studies in Business, Industry and Government Statistics, 8, 1-13.[pdf] [Code and data]
  • Xu, J., Li, W. K., and Ying, Z. (2020). Variable Screening for Survival Data in the Presence of Heterogeneous Censoring. Scandinavian Journal of Statistics, 47, 1171-1191.
  • Xu, J., Yue, M., and Zhang, W. (2020). A New Multilevel Modeling Approach for Clustered Survival Data. Econometric Theory, 36, 707-750.
  • Yuan, M., Xu, S., Yang, Y., Zhou, Y., Li, Y., Xu, J., and Pinheiro, J. (2020)z. SCEBE: an Efficient and Scalable Algorithm for Genome-wide Association Studies on Longitudinal Outcomes with Mixed effects Modeling. Briefings in Bioinformatics, bbaa130.
  • Yuan M., Li Y., Yang Y., Xu J., Tao F., Zhao L., Zhou H., Pinheiro J. and Xu S. (2020). A Novel Quantification of Information for Longitudinal Data Analyzed by Mixed-effects Modeling. Pharmaceutical Statistics, 19, 388-398.
  • Fang, Y. and Xu, J. (2020). Joint Variable Screening in the Censored Accelerated Failure Time Model. Statistica Sinica, 30, 467-485.
  • Ji, K., Tan, J., Xu, J., and Chi, Y. (2020) Learning Latent Features With Pairwise Penalties in Low-Rank Matrix Completion. IEEE Transactions on Signal Processing, 68, 4210-4225.
  • Yang, Yi. , Basu, Saonli. & Zhang, Lin. (Mar 2020). A Bayesian hierarchical variable selection prior for pathway-based GWAS using summary statistics. Statistics in medicine. 39 (6). 724 - 739. doi:10.1002/sim.8442
    [ PDF link: ]
  • McKeague, I. W. and (Henry) Zhang, X. (2020)
    Significance Testing for Canonical Correlation Analysis in High Dimensions
    [arXiv link]
  • Gyllenberg, D., McKeague, I. W., Sourander, A. and Brown, A.S. (2020)
    Robust Data-driven Identification of Risk Factors and their Interactions: A Simulation and a Study of Parental and Demographic Risk Factors for Schizophrenia
    International Journal of Methods in Psychiatric Research, 2020;29:e1834. [pdf]
  • Huang, X., Xu, J. and Tian, G. (2019). On Profile MM Algorithms for Gamma Frailty Survival Models. Statistica Sinica, 29, 895-916.
  • Tian, G., Huang, X. and Xu, J. (2019). An Assembly and Decomposition Approach for Constructing Separable Minorizing Functions in a Class of MM Algorithms. Statistica Sinica, 29, 961-982.
  • McKeague, I. W., Pekoz, E. and Swan, Y. (2019)
    Stein's Method and Approximating the Quantum Harmonic Oscillator
    Bernoulli, 25, 89-111.[pdf]
  • Huang, T.-J., McKeague, I. W. and Qian, M. (2019)
    Marginal Screening for High-Dimensional Predictors of Survival Outcomes
    Statistica Sinica, 29, 2105-2139. [pdf] [supplement]
  • Chang, H.-W. and McKeague, I. W. (2019)
    Nonparametric Testing for Multiple Survival Functions with Non-Inferiority Margins.
    The Annals of Statistics, 47, 205-232.[pdf] [supplement]
  • McKeague, I. W. (2019)
    Introduction to Empirical Likelihood (Lecture Notes)
    First prepared for a Workshop at Université catholique de Louvain, May 2002.[pdf]
  • McKeague, I. W. and Qian, M. (2019)
    Marginal Screening of 2x2 Tables in Large-Scale Case-Control Studies
    Biometrics, 75, 163-171 [pdf] [supplement] [R code]
  • Fang, Y., Xu, J. and Yang, L. (2018). Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator. Journal of Machine Learning Research, 19,1-21.
  • Yuan, M., Xu, S., Yang, Y., Xu, J., Huang, X., Tao, F., Zhao, L., Zhang, L., and Pinheiro, J. (2018). A Quick and Accurate Method for Estimation of Covariate Effects Based on Empirical Bayes Estimates in Mixed-effects Modeling: Correction of Bias Due to Shrinkage. Statistical Methods in Medical Research, 28, 3568-3578.
  • Wang, C., Shen, Q., Du, L., Xu, J., and Zhang, H. (2018). A Functional Beta Model for Detecting Age-related Genomewide DNA Methylation Marks. Statistical Methods in Medical Research, 27(9): 2627-2640.
  • Zheng, G., Xiong, J., Li, Q., Xu, J., Yuan, A., and Gastwirth, J. (2018). Evaluating the Accuracy of Small P-Values In Genetic Association Studies Using Edgeworth Expansions. Scandinavian Journal of Statistics, 45(1): 1-33.
  • Xu, S., Yuan, M., Zhu, H., Yang, Y., Wang, H., Zhou, H., Xu, J., Zhang, L. and Pinheiro, J. (2018). Full covariate modelling approach in population pharmacokinetics: understanding the underlying hypothesis tests and implications of multiplicity. Br J Clin Pharmacol, 84, 1525-1534.
  • Lala, A., Guo, Y., Xu, J., Esposito, M., Morine, K., Karas, R., Katz, S., Hochman, J., Burkhoff, D., and Kapur, N. (2018). Right Ventricular Dysfunction in Acute Myocardial Infarction Complicated by Cardiogenic Shock: A Hemodynamic Analysis of the SHould we emergently revascularize Occluded coronaries for Cardiogenic shocK (SHOCK) Trial and Registry J Card Fail, 24(3), 148-156
  • Yang, Yi. , Basu, Saonli. , Mirabello, Lisa. , Spector, Logan. & Zhang, Lin. (May 2018). A Bayesian gene-based genome-wide association study analysis of osteosarcoma trio data using a hierarchically structured prior. Cancer Informatics. 17. doi:10.1177/1176935118775103
    [ PDF link: ]
  • Wang, H. J., McKeague, I. W. and Qian, M. (2018)
    Testing for Marginal Linear Effects in Quantile Regression
    JRSS-B, 80, 433-452.[pdf] [supplement] [R code] [Help and Example] [HIV-EFV data (csv file)]
  • Hjort, N. L., McKeague, I. W. and Van Keilegom, I. (2018)
    Hybrid Combinations of Parametric and Empirical Likelihoods
    Statistica Sinica (special issue in honor of Peter Hall), 28, 389-2407. [pdf]