Selected Publications

Peng, R. & Lu, Z. (2024) Semiparametric Averaging of Nonlinear Marginal Logistic Regressions and Forecasting for Time Series Classification. Econometrics and Statistics, 31 (2024) 19–37.
[ Link: https://doi.org/10.1016/j.ecosta.2021.11.001 ]
Yang, Y., Wang, Q., Wang, C., Buxbaum, J., & Ionita-Laza, I. (2024). KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies. American Journal of Human Genetics. Advance online publication.
[ Link: https://doi.org/10.1016/j.ajhg.2024.05.003 ]
Jiao, S., Frostig, R., & Ombao, H. (2024). Filtrated Common Functional Principal Component Analysis of Multigroup Functional Data. The Annals of Applied Statistics, 18(2), 1160-1177.
[ Link: https://doi.org/10.1214/23-AOAS1827 ]
Wang, Z., Zhang, F., Zheng, C., Hu, X., Cai, M., & Yang, C. (2024). MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information. Journal of Computational and Graphical Statistics. Advance online publication.
[ Link: https://doi.org/10.1080/10618600.2024.2319160 ]
Zhang, Q., Lu, Z., Liu, S., Yang, H. & Pan, J. (2024). An MA-MRR model for transaction-level analysis of high-frequency trading processes. Journal of Management Science and Engineering, 2024, 9(1), pp. 53–61.
[ Link: https://doi.org/10.1016/j.jmse.2023.08.001 ]
Lu, Z., Ren, X. & Zhang, R. (2024). On Semiparametrically Dynamic Functional- Coefficient Autoregressive Spatio-Temporal Models with Irregular Location Wide Nonstationarity. Journal of the American Statistical Association, Volume 119, Issue 546, Pages 1032-1043.
[ Link: https://doi.org/10.1080/01621459.2022.2161386 ]
Kiakojouri, A., Lu, Z., Mirring, P., Powrie, H. & Wang, L. (2024). A generalised intelligent bearing fault diagnosis model based on a two-stage approach. Machines, 12(1), 77.
[ Link: https://doi.org/10.3390/machines12010077 ]
Peng, R. & Lu, Z. & Ge, F. (2024). On a Location-wide Semiparametric Analysis of Spatio-Temporal Dynamics of the COVID-19 Daily New Cases in the UK. Accepted for a chapter in the book Recent Advances in Econometrics and Statistics (“Festschrift in honor of Marc Hallin’s 75th birthday”), to be published by Springer.
Xu, J., & Yuan, A. (2024). Frequentist Bayesian compound inference. Statistics and its Interface, 17(1), 9-26.
[ Link: https://doi.org/10.4310/23-SII797 ]
Hu, X., Su, W., Ye, Z., & Zhao, X. (2024). Conditional modeling of panel count data with partly interval-censored failure event. Biometrics, 80(1), Article ujae020.
[ Link: https://doi.org/10.1093/biomtc/ujae020 ]
Yuan, G., Zhao, Y., Kpotufe, S.(2024), Regimes of No Gain in Multi-class Active Learning. Journal of Machine Learning Research, 25(129), 1.
[ Link: https://www.jmlr.org/papers/v25/23-0234.html ]
Barreto-Souza, W., & Chan, N. H. (2023). Nearly unstable integer-valued ARCH process and unit root testing. Scandinavian Journal of Statistics. Advance online publication.
[ Link: https://doi.org/10.1111/sjos.12689 ]
Kiakojouri, A., Lu, Z., Mirring, P., Powrie, H. & Wang, L. (2023). A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data. Sensors, 23(22), 9048.
[ Link: https://doi.org/10.3390/s23229048 ]
Wei, P., Yuan, K., Ren, X., Yan, C. & Lu, Z. (2023). Time-varying spillover networks of green bond and related financial markets. International Review of Economics & Finance, Volume 88, Pages 298-317.
[ Link: https://doi.org/10.1016/j.iref.2023.06.022 ]
Cai, M., Wang, Z., Xiao, J., Hu, X., Chen, G., & Yang, C. (2023). XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias. Nature Communications, 14(1),6870.
[ Link: https://doi.org/10.1038/s41467-023-42614-7 ]
Dai, S., & Chan, N. H. (2023). Testing of Constant Parameters for Semi-Parametric Functional Coefficient Models with Integrated Covariates. Journal of Time Series Analysis, 44(5-6), 474-486.
[ Link: https://doi.org/10.1111/jtsa.12709 ]
Loh, W-L. & Sun, S. (2023). Estimating the parameters of some common Gaussian random fields with nugget under fixed-domain asymptotics. Bernoulli. 29(3). 2519 - 2543.
[ Link: https://doi.org/10.3150/22-bej1551 ]
Chan, N. H., Li, Y., & Sit, T. (2023). Excess Mean of Power Estimator of Extreme Value Index. In Y. Liu, J. Hirukawa, & Y. Kakizawa (Eds.), Research Papers in Statistical Inference for Time Series and Related Models: Essays in Honor of Masanobu Taniguchi (pp. 25-82).
[ Link: https://doi.org/10.1007/978-981-99-0803-5_2 ]
Zhang, R., Chan, N. H., & Chi, C. (2023). Nonparametric testing for the specification of spatial trend functions. Journal of Multivariate Analysis, 196, [105180].
[ Link: https://doi.org/10.1016/j.jmva.2023.105180 ]
Zhang, H., Su, W. & Yin, G. (2023). Quasi-Rerandomization for Observational Studies. BMC Medical Research Methodology.
[ Link: https://doi.org/10.1186/s12874-023-01977-7 ]
Chen, K., Chan, N. H., Yau, C. Y., & Hu, J. (2023). Penalized Whittle likelihood for spatial data. Journal of Multivariate Analysis, 195, [105156].
[ Link: https://doi.org/10.1016/j.jmva.2023.105156 ]
Wang, X., Yang, W., Ren, X. & Lu, Z., (2023), Can financial inclusion affect energy poverty in China? Evidence from a spatial econometric analysis. International Review of Economics and Finance, Volume 85, Pages 255-269.
[ Link: https://doi.org/10.1016/j.iref.2023.01.020 ]
Lin, Z., Xue, H., & Pan, W. (2023). Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data. PLoS Genetics, 19(5), e1010762.
[ Link: https://doi.org/10.1371/journal.pgen.1010762 ]
Wang, T., Tang, W. , Lin, Y. & Su, W. (2023). Semi-supervised inference for nonparametric logistic regression. Statistics in Medicine.
[ Link: https://doi.org/10.1002/sim.9737 ]
Peng, R. & Lu, Z. (2023), Uniform consistency for local fitting of time series non-parametric regression allowing for discrete-valued response. Statistics and Its Interface, Volume 16, 305–318.
[ Link: SII-2023-0016-0002-a010.pdf (intlpress.com) ]
Lin, Z., Xue, H., & Pan, W. (2023). Robust multivariable Mendelian randomization based on constrained maximum likelihood. The American Journal of Human Genetics, 110(4), 592-605.
[ Link: https://doi.org/10.1016/j.ajhg.2023.02.014 ]
Li, C. , Sun, S. & Zhu, Y. (2023). Fixed-domain posterior contraction rates for spatial Gaussian process model with nugget. Journal of the American Statistical Association.
[ Link: https://doi.org/10.1080/01621459.2023.2191380 ]
Xue, H., Shen, X., & Pan, W. (2023). Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data. Journal of the American Statistical Association, 118(543), 1525-1537.
[ Link: https://doi.org/10.1080/01621459.2023.2183127 ]
Hao, M. , Lin, Y. , Shen, G. & Su, W. (2023). Nonparametric inference on smoothed quantile regression process. Computational Statistics & Data Analysis. 179. 107645.
[ Link: https://doi.org/10.1016/j.csda.2022.107645 ]
Hu, X., Su, W. & Zhao, X. (2023). Sieve estimation of semiparametric accelerated mean models with panel count data. Electronic Journal of Statistics. 17. 1616 - 1643.
[ Link: https://doi.org/10.1214/23-EJS2128 ]
Huang, X., Xu, J. and Zhou, Y. (2023). Efficient algorithms for survival data with multiple outcomes using the frailty model. Statistical Methods in Medical Research. 2023;32(1):118-132.
[ Link: https://doi.org/10.1177/09622802221133554 ]
Jiao, S., Chan, N. H. & Yau, C. Y. (2023). Enhanced Change Point Detection in Functional Means. Statistica Sinica. (in press).
[ Link: https://doi.org/10.48550/arXiv.2205.04299 ]
Chan, N. H., Gao, L., & Palma, W. (2022). Simultaneous variable selection and structural identification for time-varying coefficient models. Journal of Time Series Analysis, 43(4), 511-531.
[ Link: https://doi.org/10.1111/jtsa.12626 ]
Lu, S., Cheng, L., Lu, Z., Huang, Q. & Khan, B. A., (2022). A Self-Adaptive Grey DBSCAN Clustering Method. In: The Journal of Grey System, 34, 4, 98-109.
[ Link: jgrey.nuaa.edu.cn/en/abstract/abstract10119.shtml ]
Pei, Y., Peng, H., & Xu, J. (2022). A latent class Cox model for heterogeneous time-to-event data. Journal of Econometrics.
[ Link: https://doi.org/10.1016/j.jeconom.2022.08.009 ]
Liu, Li. , Su, Wen. , Yin, Guosheng. , Zhao, Xingqiu. & Zhang, Ying. (2022). Nonparametric inference for reversed mean models with panel count data. Bernoulli. 28. 2968 - 2997.
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.
[ Link: https://doi.org/10.1016/j.ajhg.2022.08.013 ]
Kiakojouri, A., Lu, Z., Mirring, P., Powrie, H. E. G. & Wang, L. (2022), A generalised machine learning model based on multinomial logistic regression and frequency features for rolling bearing fault classification. Insight, 64, 8, p. 447-452.
Wang, X., Li, J., Ren, X. & Lu, Z. (2022), Exploring the bidirectional causality between green markets and economic policy: Evidence from the time-varying Granger test. Environmental Science and Pollution Research, 29, pages 88131–88146.
[ Link: https://doi.org/10.1007/s11356-022-21685-x ] (The Impact factor is 5.19 in 2024)
Xiao, Jiashun , Cai, Mingxuan , Yu, Xinyi , Hu, Xianghong , Chen, Gang , Wan, Xiang & Yang, Can (2022). Leveraging the local genetic structure for trans-ancestry association mapping. The American Journal of Human Genetics. 109(7). 1317 - 1337.
[ Link: https://doi.org/10.1016/j.ajhg.2022.05.013 ]
Huang, H-H., Chan, N. H., Chen, K., & Ing, C-K. (2022). Consistent Order Selection for Arfima Processes. Annals of Statistics, 50(3), 1297-1319.
[ Link: https://doi.org/10.1214/21-AOS2149 ]
Ren, X., Li, Y., Yan, C., Wen, F. & Lu, Z. (2022). The interrelationship between the carbon futures market and the green bonds market: Evidence from wavelet and quantile-on-quantile methods. Technological Forecasting and Social Change, Volume 179, June 2022, 121611.
[ Link: https://doi.org/10.1016/j.techfore.2022.121611 ] (The Impact Factor is 12 in 2024)
Su, Wen. , Yin, Guosheng. , Zhang, Jing. & Zhao, Xingqiu. (2022). Divide-and-conquer for accelerated failure time model with massive time-to-event data. Canadian Journal of Statistics. 51. 400 - 419.
Su, Wen. , He, Baihua. , Zhang, Yan Dora. & Yin, Guosheng. (2022). C-index regression for recurrent event data. Contemporary Clinical Trials. 118. 106787
Li, Z., Xu, J., Zhou, W., and Zhao, N. (2022). Penalized Jackknife Empirical Likelihood in High Dimensions. Statistica Sinica. In press.
Xue, H. and Pan, W. (2022). Robust inference of bi-directional causal relationships in presence of correlated pleiotropy with GWAS summary data. PLoS Genetics, 18(5), e1010205.
[ Link: https://doi.org/10.1371/journal.pgen.1010205 ]
Ren, X., Li, Y., Shahbaz, M., Dong, K. & Lu, Z. (2022) Climate risk and corporate environmental performance: Empirical evidence from China. Sustainable Production and Consumption, 30, 467-477.
[ Link: https://doi.org/10.1016/j.spc.2021.12.023 ] (The Impact Factor is 10.9 in 2024)
Kpotufe, S., Yuan, G., Zhao, Y. (2022). Nuances in margin conditions determine gains in active learning. International Conference on Artificial Intelligence and Statistics, PMLR, 8112– 8126.
[ Link: https://proceedings.mlr.press/v151/kpotufe22a.html ]
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
[ Link: https://onlinelibrary.wiley.com/doi/pdf/10.1002/gepi.22437 ]
Xiao, Jiashun, Cai, Mingxuan, Hu, Xianghong, Wan, Xiang, Chen, Gang & Yang, Can (2022). XPXP: Improving polygenic prediction by cross-population and cross-phenotype analysis. Bioinformatics. 38(7). 1947 - 1955.
[ Link: https://doi.org/10.1093/bioinformatics/btac029 ]
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]
Jiao, S., Frostig, R. & Ombao, H. (2022). Filtrated Common Functional Principal Component Analysis for Multi-group Functional Data. Annals of Applied Statistics. (revised).
[ Link: https://doi.org/10.48550/arXiv.2205.04299 ]
Jiao, S., Frostig, R. & Ombao, H. (2022). Break Point Detection for Functional Covariance. Scandinavia Journal of Statistics. 50/2. 477 - 512.
Jiao, S., Frostig, R. & Ombao, H. (2022). Variation Pattern Classification of Functional Data. Canadian Journal of Statistics. (online).
[ Link: https://doi.org/10.1002/cjs.11738 ]
Zhang, R., & Chan, N. H. (2021). Nonstationary linear processes with infinite variance garch errors. Econometric Theory, 37(5), 892-925.
[ Link: https://doi.org/10.1017/S0266466620000377 ]
Chan, N. H., Ng, W. L., Yau, C. Y., & Yu, H. (2021). Optimal change-point estimation in time series. Annals of Statistics, 49(4), 2336-2355.
[ Link: https://doi.org/10.1214/20-AOS2039 ]
Li, Y., Chan, N. H., Yau, C. Y., & Zhang, R. (2021). Group orthogonal greedy algorithm for change-point estimation of multivariate time series. Journal of Statistical Planning and Inference, 212, 14-33.
[ Link: https://doi.org/10.1016/j.jspi.2020.08.002 ]
Loh, W-L. , Sun, S. & Wen, J. (Dec 2021). On fixed-domain asymptotics, parameter estimation and isotropic Gaussian random fields with Matérn covariance functions. Annals of Statistics. 49(6). 3127 - 3152.
[ Link: https://doi.org/10.1214/21-AOS2077 ]
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.
Tang, C., Yuan, G., Zheng T. (2021), Weakly Supervised Learning Creates a Fusion of Modeling Cultures. Observational Studies, 7(1), 203-211.
[ Link: https://muse.jhu.edu/article/799736 ]
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
[ Link: https://onlinelibrary.wiley.com/doi/pdf/10.1002/gepi.22379 ]
Cai, Mingxuan, Xiao, Jiashun, Zhang, Shunkang, Wan, Xiang, Zhao, Hongyu, Chen, Gang & Yang, Can (2021). A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. The American Journal of Human Genetics. 108(4). 632 - 655.
[ Link: https://doi.org/10.1016/j.ajhg.2021.03.002 ]
Chan, N. H., Ng, W. L., & Yau, C. Y. (2021). A self-normalized approach to sequential change-point detection for time series. Statistica Sinica, 31(1), 491-517.
[ Link: https://doi.org/10.5705/ss.202018.0269 ]
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]
Jiao, S., Aue, A. & Ombao, H. (2021). Functional Time Series Prediction Under Partial Observation of the Future Curve. Journal of the American Statistical Association. 118/541. 315 - 316.
Jiao, S. & Ombao, H. (2021). Shape-preserving Prediction for Stationary Functional Time Series. Electronic Journal of Statistics. 15/2. 3996 - 4026.
Xue, H., Shen, X., & Pan, W. (2021). Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects. The American Journal of Human Genetics, 108(7), 1251-1269.
[ Link: https://doi.org/10.1016/j.ajhg.2021.05.014 ]
Wen, J., Sun, S. & Loh, W-L. (2021). Smoothness estimation of nonstationary Gaussian random fields from irregularly spaced data observed along a curve. Electronic Journal of Statistics. 15(2). 6071 - 6150.
Liu, Li. , Su, Wen. & Zhao, Xingqiu. (2021). Bi-selection in the high-dimensional additive hazards regression model. Electronic Journal of Statistics. 15. 748 - 772.
Xue, H., & Pan, W. (2020). Inferring causal direction between two traits in the presence of horizontal pleiotropy with GWAS summary data. PLoS Genetics, 16(11), e1009105.
[ Link: https://doi.org/10.1371/journal.pgen.1009105 ]
Chan, N. H., Ling, S., & Yau, C. Y. (2020). LASSO-BASED VARIABLE SELECTION OF ARMA MODELS. Statistica Sinica, 30(4), 1925-1948.
[ Link: https://doi.org/10.5705/ss.202017.0500 ]
Chen, K., Chan, N.H. & Yau, C. Y. (2020) Bartlett correction of frequency domain empirical likelihood for time series with unknown innovation variance. Annals of the Institute of Statistical Mathematics 72 5 1159-1173;
[ Link: https://doi.org/10.1007/s10463-019-00723-5 ]
Yang, W., Lu, Z., Wang, D., Shao, Y. & Shi, J. (2020). Sustainable Evolution of China’s Regional Energy Efficiency Based on a Weighted SBM Model with Energy Substitutability. Sustainability , 12, 10073.
[ Link: https://doi.org/10.3390/su122310073 ] (IF= 3.889 in Year 2024)
Ng, C. T., Shi, Y., & Chan, N. H. (2020). Markowitz portfolio and the blur of history. International Journal of Theoretical and Applied Finance, 23(5), Article 2050030. Advance online publication.
[ Link: https://doi.org/10.1142/S0219024920500302 ]
Sun, Y., Lian, G., Lu, Z., Loveland, J., & Blackhurst, I. (2020). Modeling the variance of return intervals toward volatility prediction. Journal of Time Series Analysis, 41(4), 492-519.
[ Link: https://doi.org/10.1111/jtsa.12518 ]
Muhammad, M. & Lu, Z. (2020). Estimating the UK index flood: an improved spatial flooding analysis. Environmental Modelling and Assessment, 25, 731–748.
[ Link: https://doi.org/10.1007/s10666-020-09713-x ] (IF= 2.016 in Year 2023-24)
Chan, N. H., Cheung, S. K. C., & Wong, S. P. S. (2020). Inference for the degree distributions of preferential attachment networks with zero-degree nodes. Journal of Econometrics, 216(1), 220-234. Advance online publication.
[ Link: https://doi.org/10.1016/j.jeconom.2020.01.015 ]
Jiang, Z., Ling, N., Lu, Z., Tjøstheim, D., & Zhang, Q. (2020). On bandwidth choice for spatial data density estimation. Journal of the Royal Statistical Society. Series B: Methodological, vol. 82(3), pages 817-840.
[ Link: https://doi.org/10.1111/rssb.12367 ] (IF= 5.8, Year 2023)
Lu, Z. & Ren, X. (2020). Statistical Intelligent Modelling: Some Personal Thinking on Artificial Intelligence from the Perspective of Statistics. Chapter 4, Artificial Intelligence and Development of Future Society (Editor-in-Chief: Yike Guo), pages 44-58. Beijing: Scientific and Technical Document Press. [In Chinese] (Organized by Association of British Chinese Professors)
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
[ Link: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8442 ]
Cai, Mingxuan, Chen, Lin S., Liu, Jin & Yang, Can (2020). IGREX for quantifying the impact of genetically regulated expression on phenotypes. NAR Genomics and Bioinformatics. 2(1). lqaa010
[ Link: https://doi.org/10.1093/nargab/lqaa010 ]
Cai, Mingxuan, Dai, Mingwei, Ming, Jingsi, Peng, Heng, Liu, Jin & Yang, Can (2020). BIVAS: a scalable Bayesian method for bi-level variable selection with applications. Journal of Computational and Graphical Statistics. 29(1). 40 - 52.
[ Link: https://doi.org/10.1080/10618600.2019.1624365 ]
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
[ Link: https://journals.sagepub.com/doi/pdf/10.1177/1176935118775103 ]
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