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: https://doi.org/10.1016/j.ajhg.2022.08.013 ]
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: https://onlinelibrary.wiley.com/doi/pdf/10.1002/gepi.22437 ]
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:https://onlinelibrary.wiley.com/doi/pdf/10.1002/gepi.22379 ]
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 CascadesStatistical 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 OutcomesCase 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: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8442 ]
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 SchizophreniaInternational 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 OscillatorBernoulli, 25, 89-111.[pdf]
Huang, T.-J., McKeague, I. W. and Qian, M. (2019) Marginal Screening for High-Dimensional Predictors of Survival OutcomesStatistica 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 StudiesBiometrics, 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: https://journals.sagepub.com/doi/pdf/10.1177/1176935118775103 ]
Hjort, N. L., McKeague, I. W. and Van Keilegom, I. (2018) Hybrid Combinations of Parametric and Empirical LikelihoodsStatistica Sinica (special issue in honor of Peter Hall), 28, 389-2407. [pdf]
