University of Alberta Department of Mathematical and Statistical Sciences
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Peer-reviewed * indicates trainees supervised; co-first authors are underlined

Refereed Journal Papers:

  1. Xie, J.*, Ding, X., Jiang, B., Yan, X., and Kong, L., (2022). High dimensional model averaging for quantile regression, Canadian Journal of Statistics, invited revision submitted.
  2. Tu, W., Fu, F., Cobzas, D., Kong, L., Jiang, B. and Huang, C. (2022). Low Rank plus Sparse Decomposition of fMRI Data with Application to Alzheimer's Disease. Frontiers in Neurosciences, accepted.
  3. Pietrosanu, M.*, Shu, H., Jiang, B., Kong, L., Heo, G. He, Q., Gilmore, J., and Zhu, H. (2021). Estimation for bivariate quantile varying coefficient model with application in diffusion tensor imaging data analysis. Biostatistics, in press.
  4. Jiang, B., Raftery, A., Steele, R., and Wang, N. (2021). Balancing Inferential Integrity and Disclosure Risk via Model Targeted Masking and Multiple Imputation, Journal of American Statistical Association, in press.
  5. Hu, S., Alshehabi AI-Ani, J., Hughes, K., Denier, N., Konnikov, A., Ding, L.*, Xie, J.*, Hu, Y., Tarafdar, M., Jiang, B., Kong, L. and Dai, H. (2022). Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation. Frontiers in Big Data, Vol 5. 805713.
  6. Pietrosanu, M.*, Kong, L., Yuan, Y., Bell, R., Letourneau, N. and Jiang, B. (2022). Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. Entropy, 24, 232.
  7. Liu, M.*, Pietrasonu, M., Kong, L., Jiang, B. and Zhou, X. (2022). Reproducing Kernel based Partial Functional Expectile Regression. Canadian Journal of Statistics, 50(1), 241-266.
  8. Lee C.H., Jiang, B., Nakhaei-Nejad, M., Barilla, D., Blevins, G. and Giuliani, F. (2021). Cross-Sectional Analysis of Peripheral Blood Mononuclear Cells in Lymphopenic and Non-lymphopenic Relapsing Remitting Multiple Sclerosis Patients Treated with Dimethyl Fumarate. Multiple Sclerosis and Related Disorders, 52, 103003.
  9. Li, C., Tong, C.L.*, Niu, D., Jiang, B., Zuo, X., Cheng, L., Xiong, J., and Yang, J. (2021). Similarity Embedding Networks for Robust Human Activity Recognition. ACM Transactions on Knowledge Discovery from Data (TKDD), 15(6), 1-17.
  10. Pietrosanu, M.*, and Jiang, B. (2021). Discussion of Statistical Disease Mapping for Heterogeneous Neuroimaging Studies. Canadian Journal of Statistics, 49(1), 39-42.
  11. Pietrosanu, M.*, Gao J., Kong, L., Jiang, B., and Niu, D. (2021). Advanced algorithms for penalized quantile and composite quantile regression. Computational Statistics, 36(1), 333-346.
  12. Su, T., Wang, Y.*, Liu, Y., Branton, W., Asahchop, E., Power, C., Jiang, B., and Kong, L. (2020). Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions. Entropy, 22(11), 1257.
  13. Vekhande, C., Jiang, B., and Kate, M. (2020). Screening for Cognitive Impairment, Being Cognizant of the Liminal Deities and Demons. Canadian Journal of Neurological Sciences, 47(6), 731-733.
  14. Jiang, B., Petkova, E., Tarpey, T. and Ogden R. T. (2020). A Bayesian Approach to Joint Modeling of Matrix-valued Imaging Data and Treatment Outcome with Applications to Depression Studies. Biometrics, 76(1): 87-97.
  15. Wang, Y.*, Kong, L., Jiang, B., Zhou, X., Yu, S., Zhang, L., Heo, G.(2019). Wavelet-based Lasso in Functional Linear Quantile Regression. Journal of Statistical Computation and Simulation, 89:6, 1111-1130.
  16. Zhang, L., Yu, D., Jiang, B., Mizera, I. and Kong, L. (2019). Sparse Wavelet Estimation in Quantile Regression with Multiple Functional Predictors. Computational Statistics & Data Analysis, 136: 12-29.
  17. Petkova, E., Tarpey, T., Ogden R. T., Ciarleglio, A., Jiang, B., et. al. (2017). Statistical analysis plan for stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) study. Contemporary Clinical Trials Communications, Vol. 6, 22-30.
  18. Jiang, B., Petkova, E., Tarpey, T. and Ogden R. T. (2017). Latent class modeling using matrix covariates with application to identifying early placebo responders based on EEG signals. The Annals of Applied Statistics, Vol. 11, No. 3, 1513-1536.
  19. Jiang, B., Elliott, M. R., Sammel, M. D. and Wang, N. (2016). Bayesian Model Assessments in Evaluating Mixtures of Longitudinal Trajectories and Their Associations with Cross-Sectional Health Outcomes. Statistics and Its Interface, Vol. 9, 183-201.
  20. Jiang, B., Sammel, M. D., Freeman, E. W. and Wang, N. (2015). Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period. BMC Medical Research Methodology, Vol. 15, 106.
  21. Jiang, B., Elliott, M. R., Sammel, M. D. and Wang, N. (2015). Joint Modeling of Cross- Sectional Health Outcomes and Longitudinal Predictors via Mixtures of Means and Variances. Biometrics, Vol. 71(2), 487-497.
  22. Jiang, B., Wang, N., Sammel, M. D. and Elliott, M. R. (2015). Modeling Short- and Long- Term Characteristics of Follicle Stimulating Hormone as Predictors of Severe Hot Flashes in Penn Ovarian Aging Study. Journal of the Royal Statistical Society: Series C. Vol. 64(5), 731-753.
  23. Jiang, B. and Carriere, K. C. (2014). Smoothing Age-Period-Cohort Models: A Generalized Additive Model Approach. Statistics in Medicine, Vol. 33(4), 595-606.
  24. Sadowski, D. C., Ackah, F., Jiang, B. and Svenson, L. W. (2010). Achalasia: Incidence, Prevalence and Survival. A Population Based Study. Nerogastroenterology and Motility, Vol. 22, 256-261.
  25. Cree, M., Lalji, M., Jiang, B. and Carriere, K. C. (2009). Under-reporting of Compensable Mesothelioma in Alberta. American Journal of Industrial Medicine, Vol. 52, 526-533.
  26. Cree, M., Lalji, M., Jiang, B., Carriere, K. C., Beach, J. and Kamruzzaman, A. (2009). Explaining Albertas Rising Mesothelioma Rates. Chronic Diseases in Canada, Vol. 29(4): 144-152.

Refereed Conference Proceedings:

  1. Ding, L.*, Yu, D.*, Xie, J.*, Guo, W.*, Hu, S., Liu, M.*, Kong, L. Dai, H., Bao, Y., Jiang, B. (2022) Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. Proceedings of the 2022 AAAI Conference on Artificial Intelligence, accepted.
  2. Wang, Y.*, Pan, B., Tu, W., Liu, P., Jiang, B., Gao, C., Lu, W. Jiu, S. and Kong, L. (2022). Sample Average Approximation for Dependent Data: Performance Guarantee and Tractability. Proceedings of the 2022 AAAI Conference on Artificial Intelligence, accepted.
  3. Sun, K., Wang, Y.*, Liu, Y., Zhao, Y., Pan, B., Jui, S., Jiang, B. and Kong, L. (2021). Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. Proceedings of NeurIPS 2021 Conference.
  4. Tu, W., Liu, P*., Liu, Y., Kong, L., Li, G., Jiang, B., Yao, H., and Jui, S. (2021). Nonsmooth Lowrank Matrix Recovery: Methodology,Theory and Algorithm. Proceedings of the Future Technologies Conference (FTC) 2021
  5. Li, C., Niu, D., Jiang, B., Zuo, X., and Yang, J. (2021). Meta-HAR: Federated Representation Learning for Human Activity Recognition. The Web Conference (WWW) 2021 (acceptance rate: 20.6%).
  6. Hu, Y., Liu, P.*, Ge, K., Kong, L., Jiang, B. and Niu, D. (2020). Learning Privately over Distributed Features: An ADMM Sharing Approach. NeurIPS-SpicyFL 2020.
  7. Tu, W., Liu, P.*, Zhao, J., Liu, Y., Kong, L., Li, G., Jiang, B., Tian, G., and Yao, H. (2019). M-estimation in Low-rank Matrix Factorization: a General Framework. 2019 IEEE 19th International Conference on Data Mining (ICDM), 568-577 (acceptance rate: 9.08%).
  8. Yao, H., Zhu, D., Jiang, B., Yu, P.* (2019). Negative Log Likelihood Ratio Loss for Deep Neural Network Classification, Proceedings of the Future Technologies Conference (FTC) 2019, 276-282.

Book Chapters:

  1. Konnikov, A., Rets, I., Hughes, K., Alshehabi Al-Ani, J., Denier, N., Ding, L.*, Hu, S., Hu, Y., Jiang, B., Kong, L., Tarafdar, M. and Yu, D.* (2022). Responsible AI for labour market equality (BIAS). In: How to Manage International Multidisciplinary Research Projects. Edward Elgar, Cheltenham. (In Press).
  2. Wang, Y.*, Lai, T., Jiang, B., Kong, L.and Zhang Z. (2022). Functional linear regression for partially observed functional data, Springer book series, (In Press).

PhD Thesis

  1. Jiang, B. (2014). Bayesian Joint Modeling of Longitudinal Trajectories and Health Outcome: A Broad Evaluation of Mean and Variation Features in Risk Profiles and Model Assessments. Ph.D. thesis, Advisor: Michael R. Elliott and Naisyin Wang, University of Michigan.

MSc Thesis

  1. Jiang, B. (2008). Estimation for Age-Period-Cohort Models: with Application to the Pleural Mesothelioma Data in Alberta 1980 to 2004. MSc thesis, Advisor: K. C. Carriere, University of Alberta.