Publications
] , Submitted. Two Gaussian regularization methods for time-varying networks. Available at: http://arxiv.org/abs/2202.07099.
, Accepted. Dependence model assessment and selection with DecoupleNets. Journal of Computational and Graphical Statistics. Available at: https://doi.org/10.1080/10618600.2022.2157835.
, Accepted. RafterNet: Probabilistic predictions in multi-response regression. The American Statistician. Available at: https://doi.org/10.1080/00031305.2022.2141857.
, 2023. Essential Statistics for Data Science: A Concise Crash Course, Oxford University Press. Available at: https://doi.org/10.1093/oso/9780192867735.001.0001.
, 2022. Applications of multivariate quasi-random sampling with neural networks. In Monte Carlo and Quasi-Monte Carlo Methods, MCQMC 2020. Springer, pp. 273–289. Available at: https://doi.org/10.1007/978-3-030-98319-2_14.
, 2022. Multivariate time-series modeling with generative neural networks. Econometrics and Statistics, 23, pp.147–164. Available at: https://doi.org/10.1016/j.ecosta.2021.10.011.
, 2021. Quasi-random sampling for multivariate distributions via generative neural networks. Journal of Computational and Graphical Statistics, 30, pp.647–670. Available at: http://doi.org/10.1080/10618600.2020.1868302.
, 2021. First-order correction of statistical significance for screening two-way epistatic interactions. In Epistasis: Methods and Protocols. Springer, pp. 181–190. Available at: http://doi.org/10.1007/978-1-0716-0947-7_12.
, 2020. High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition. Canadian Journal of Statistics, 48, pp.308–337. Available at: http://doi.org/10.1002/cjs.11532.
, 2019. On fitting complex models to noisy data. In Proceedings of the International Conference on Statistics: Theory and Applications. pp. 34.1–34.7. Available at: http://doi.org/10.11159/icsta19.34.
, 2019. A framework for measuring association of random vectors via collapsed random variables. Journal of Multivariate Analysis, 172, pp.5–27. Available at: http://doi.org/10.1016/j.jmva.2019.02.012.
, 2019. Pruning variable selection ensembles. Statistical Analysis and Data Mining: The ASA Data Science Journal, 12, pp.168–184. Available at: http://doi.org/10.1002/sam.11410.
, 2019. Compositional epistasis detection using a few prototype disease models. PLOS ONE, 14, p.e0213236. Available at: http://doi.org/10.1371/journal.pone.0213236.
, 2019. Quadratic discriminant analysis for high-dimensional data. Statistica Sinica, 29, pp.939–960. Available at: http://doi.org/10.5705/ss.202016.0034.
, 2017. A continuous-time stochastic block model for basketball networks. Annals of Applied Statistics, 11, pp.553–597. Available at: http://doi.org/10.1214/16-AOAS993.
, 2016. Expanded alternating optimization for matrix factorization and penalized regression. In Proceedings of the 22nd International Conference on Computational Statistics. pp. 217–229. Available at: http://www.compstat2016.org/docs/COMPSTAT2016_proceedings.pdf?20160807205859.
, 2016. Tyranny in the name of science. Ottawa Citizen, September 8, 2016, p.A8. Available at: http://ottawacitizen.com/opinion/columnists/zhu-lets-avoid-tyranny-over-data-collection.
, 2015. Opinion versus practice regarding the use of rehabilitation services in home care: an investigation using machine learning algorithms. BMC Medical Informatics and Decision Making, 15, p.80. Available at: http://doi.org/10.1186/s12911-015-0203-1.
, 2015. Rehabilitation therapies for older clients of the Ontario home care system: regional variation and client-level predictors of service provision. Disability and Rehabilitation, 37, pp.625–631. Available at: http://doi.org/10.3109/09638288.2014.935494.
