University of Alberta Department of Mathematical and Statistical Sciences
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Peer-reviewed Journals +, *, and - indicate equal contributions, corresponding author, and trainees supervised, respectively.
[1]. Zhang, Z., Wang, X., Kong, L., and Zhu, H. (2019+) . High-Dimensional Spatial Quantile Function-on-Scalar Regression. Journal of the American Statistical Association, invited revision submitted.
[2]. Tu, W.-, Chen, P., Koenig, N., Gomez, D.,Fujiwara, E., Gill, J., Kong, L.*, and Power, C.*. (2019+) . Machine learning models reveal neurocognitive impairment type and prevalence are associated with distinct variables in HIV/AIDS. Journal of NeuroVirology, invited revision submitted.
[3]. Subhan, F., Shulman, L.-, Yuan, Y., McCargar, L., Kong, L., and Bell, R. (2019+). Fat Mass istribution and Accretion During Pregnancy and Early Postpartum - A Prospective Study of Albertan Women. BMJ Open, accepted.
[4]. Tang, Q., Kong, L., Karunamuni, R. and Ruppert, D. (2019+). Partial Functional Partially Linear Single Index Model. Statistica Sinica, accepted.
[5]. Karunamuni, R., Kong, L. and Tu, W.- (2019+). Efficient Robust Doubly Adaptive Regularized Regression. Statistical Methods in Medical Research, Accepted. [PDF]
[6]. Liu, B.-, Mavrin, B.-, Kong, L., and Niu, D. (2019). Spatial Data Reconstruction via ADMM and Spatial Spline Regression. Applied Sciences, Vol. 9, No. 9, 1733. [PDF]
[7]. Yu, D.+-, Zhang, L.+, Jiang, B., Mizera, I. and Kong, L.* (2019). Sparse Wavelet Estimation in Quantile Regression with Multiple Functional Predictors. Computational Statistics & Data Analysis, Vol. 136, 12-29. [PDF]
[8]. Tu, W.-, Kong, L., Karunamuni, R., Butcher, K., Zheng, L.-, and McCourt, R. (2019). Non-local Spatial Clustering in Automated Brain Hematoma and Edema Segmentation. Applied Stochastic Models in Business and Industry, Vol. 35, 321-329. [PDF]
[9]. Han, P., Kong, L.*, Zhao, J. and Zhou, X. (2019). A General Framework for Quantile Estimation with Incomplete Data. Journal of Royal Statistical Society: Series B. Vol. 81, P. 2, 305-333. [PDF]
[10]. Wang, Y.-, Kong, L.*, Jiang, B., Zhou, X., Yu, S.-, Zhang, L., and Heo, G. (2019). Wavelet-based Lasso in Functional Linear Quantile Regression. Journal of Statistical Computation and Simulation, Vol. 89, No. 6, 1111-1130. [PDF]
[11]. Nathoo, F., Kong, L., and Zhu, H. (2019). A Review of Statistical Methods in Imaging Genetics. Canadian Journal of Statistics, Vol. 47, No. 1, 108-131. [PDF]
[12]. Asahchop, E., Branton, W., Krishnan,A., Chen, P., Yang, D.-, Kong, L., Zochodne, D., Brew, B., Gill, J., and Power, C. (2018). microRNA-455-3p predicts HIV-associated symptomatic distal sensory polyneuropathy and suppresses NGF expression in human neurons. JCI insight, 3(23): e122450. [PDF]
[13]. Zhang, L., Cobza, B., Wilman, A. And Kong, L. (2018). Significant Anatomy Detection through Sparse Classification: A Comparative Study. IEEE Transition in Medical Imaging, Vol. 37, No. 1, 128-137. [PDF]
[14]. Che, M.-, Kong, L., Bell, R. and Yuan, Y. (2017). Trajectory Modeling of Gestational Weight: a Functional Principal Component Analysis Approach. PLoS ONE, Vol. 12, No. 10, e0186761. [PDF]
[15]. Tang, Q. and Kong, L. (2017). Quantile regression in functional linear semiparametric model. Statistics, Vol. 51, No. 6, 1342-1358. [PDF]
[16]. Yu, D.-, Kong, L.* and Mizera, I. (2016). Partial Functional Linear Quantile Regression for Neuroimaging Data Analysis. Neurocomputing, Vol. 195, 74-87. [PDF]
[17]. He, Q.+, Kong, L.+, Wang, Y. Wang, S. Chan, T. and Holland, E. (2016). Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits. Computational Statistics & Data Analysis, Vol. 95, 222-239. [PDF]
[18]. Kong, L. and Wiens, P. D. (2015). Model-Robust Designs for Quantile Regression. Journal of the American Statistical Association, Vol. 110, No. 509, 233-245. [PDF]
[19]. Zhu, H., Fan, J. and Kong, L. (2014). Spatially Varying Coefficient Model for Neuroimaging Data with Jump Discontinuities. Journal of the American Statistical Association, Vol. 109, No. 507, 1084-1098. [PDF]
[20]. Ford, A., An, H., Kong, L., Zhu, H., Vo, K., Powers, W., and Lin, W. (2014). Clinically-relevant reperfusion in acute ischemic stroke: MTT performs better than Tmax and TTP. Translational Stroke Research, Vol. 5, 415-421. [PDF]
[21]. Calderon-Garciduenas, L., Mora-Tiscareno, A., Torres-Jardon, R., Pean-Cruz, B., Palacios-Lopez, C., Zhu, H., Kong, L., Mendoza-Mendoza, N., Montesinos-Correa, H., Romero, L., Valencia-Salazar, G., Cross, J., Kavanaugh, M., Medina-Cortina, H., Frenk, S. (2013). Exposure to Urban Air Pollution and Bone Health in Clinically Healthy Six Years Old Children. Archives of Industrial Hygiene and Toxicology, Vol. 64(1), 23-34. [PDF]
[22]. Zhu, H., Li, R. and Kong, L. (2012). Multivariate Varying Coefficient Models for Functional Responses. Annals of Statistics, Vol. 40, No. 5, 2634-2666. [PDF]
[23]. Kong, L. and Mizera, I. (2012). Quantile Tomography: Using Quantiles with Multivariate Data. Statsitica Sinica, Vol. 22, No. 4. 1589-1610. [PDF]
[24]. Zhu, H., Kong, L., Li, R., Styner, M., Gerig, G., Li, Y. and Gilmore, JH. (2011). FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics. Neuroimage, Vol. 56(3), 1412-1425. [PDF]
[25]. Kong, L. and Zuo, Y. (2010). Depth Contours Characterize the Underlying Distribution. Journal of Multivariate Analysis, Vol. 101(9), 2222-2226. [PDF]
[26]. Kong, L. and Mizera, I. (2010). Discussion of "Multivariate Quantiles and Multiple- Output Regression Quantiles: From L1 Optimization to Halfspace Depth". Annals of Statistics, Vol. 38, No. 2, 685-693. [PDF]
Peer-Reviewed Proceedings
[27]. Tu, W.+-, Yang, D+-., Che, M.-, Shi, Q.-, Li, G., Tian, G., and Kong, L.*. (2019). Ensemble-based Ultrahigh-dimensional Variable Screening, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19). (acceptance rate: 17.9%) [PDF]
[28]. Mavrin, B.-, Zhang, S., Yao, H., Kong, L.., Wu, K., and Yu, Y. (2019). Distributional Reinforcement Learning for Efficient Exploration, Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML-19). (acceptance rate: 22.6%) [PDF]
[29]. Mavrin, B.-, Zhang, S., Yao, H., and Kong, L.. (2019). Exploration in the face of Parametric and Intrinsic Uncertainties, Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19). [PDF]
[30]. Zhang, S., Mavrin, B.-, Kong, L. Liu, B. and Yao, H. (2019). QUOTA: The Quantile Option Architecture for Reinforcement Learning, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-19). (acceptance rate: 16.2%) [PDF]
[31]. Liu, B.-, Mavrin, B.-, Niu, D., and Kong, L. (2017). Recover Fine-Grained Spatial Data from Coarse Aggregation. 2017 IEEE 17th International Conference on Data Mining (ICDM 2017). (acceptance rate: 19.9%) [PDF]
[32]. Liu, B.-, Niu, D., Lai, K., Kong, L. and Xu, Y. (2017). Growing Story Forest Online from Massive Breaking News. Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM 2017). [PDF]
[33]. Zhang, L., Cobza, D., Wilman, A., and Kong, L. (2017). An unbiased penalty for sparse classification with application to neuroimaging data. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2017), Lecture Notes in Computer Science, 2017, Springer Berlin/Heidelberg, Vol. 10435, 55-63. (acceptance rate: 32.2%) [PDF]
[34]. Zhu, R., Niu, D., Kong, L., and Li, Z. (2017). Expectile Matrix Factorization for Extreme Data Analysis. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), 259-265. (acceptance rate: 24.6%) [PDF]
[35]. Liu, B.+-, Mavrin, B.+-, Niu, D., and Kong, L. (2016). House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis. 2016 IEEE 16th International Conference on Data Mining (ICDM 2016), 2047-2052. (acceptance rate: 19.6%) [PDF]
[36]. Luo, X., Zhu, L., Kong, L., and Zhu, H. (2015). Multivariate Varying Coefficient Models for DTI Tract Statistics. Functional Nonlinear Mixed Effects Models for Longitudinal Image Data, Information Processing in Medical Imaging (IPMI 2015), Lecture Notes in Computer Science}, Springer Berlin/Heidelberg, Vol. 9123, 794-805. (acceptance rate: 32.3%) [PDF]
[37]. Zhu, H., Styner, M., Li, Y., Kong, L., Shi, W., Lin, W., Coe, C. and Gilmore, JH.(2010). Multivariate Varying Coefficient Models for DTI Tract Statistics. Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010), Lecture Notes in Computer Science, 2010, Springer Berlin/Heidelberg, Vol. 6361, 690-697. (acceptance rate: 31.9%) [PDF]
Non Peer-Reviewed Proceedings
[38]. Kong, L. , and Wiens, D. (2016). Nonlinear Quantile Regression Design, Joint Statistical Meeting Proceedings, 3602-3609. [PDF]
Technical Reports
[39]. Squires, J., Kong, L., Brooker, S.Mitchell, A. Sales, A. and Estabrooks, C. (2009). Examining the Role of Context in Alzheimer Care Centers: A Pilot Study Technical Report. (Report No. 08-04-TR). Edmonton, AB, Faculty of Nursing, University of Alberta. (ISBN:978-1-55195-237-6). [PDF]
[40]. Estabrooks, C., Squires, J., Adachi, A., Kong, L. and Norton, P. (2008). Utilization of Health Research in Acute Care Settings in Alberta Technical Report. (Report No. 08-01-TR). Edmonton, AB, Faculty of Nursing, University of Alberta. (ISBN: 978-1-55195-231-4). [PDF]
[41]. Hutchinson, A., Adachi, A., Kong, L., Estabrooks, C. and Steves, B. (2008). Context and Research Use in the Care of Children: A Pilot Study Project 2 CIHR Team in Children's Pain Technical Report. (Report No. 08-03-TR). Edmonton, AB, Faculty of Nursing, University of Alberta. (ISBN: 978-1-55195-236-9). [PDF]
PhD Thesis
[42]. Kong, L. (2009). On multivariate quantile regression: directional approach and application with growth charts. Ph.D. thesis, Advisor: Ivan Mizera, University of Alberta. [PDF]