Linglong Kong, PhD
Professor, Canada Research Chair in Statistical Laerning, and Canada CIFAR AI Chair, Faculty of Science - Mathematics & Statistical Sciences
Functional and Neuroimaging data analysis, Robust statistics and quantile regression, Statistical machine learning, and AI in smart health.
Approximation techniques and asymptotic methods in statistics. Topics may include second and higher order expansions, asymptotics of likelihood based estimation and testing. Edgeworth expansions, exponential tilting, asymptotic relative efficiency, U-, M-, L-, and R-estimation. Prerequisites: STAT 566 or 664 and 512 or the equivalent.
Comment on "Measuring Housing Vitality from Multi-source Big Data and Machine Learning"
Tu, W., Jiang, B., and Kong, L.
Journal of the American Statistical Association. 2022 October; 117 (539):1060-1062
High-dimensional spatial quantile function-on-scalar regression
Z Zhang, X Wang, L Kong, H Zhu
Journal of the American Statistical Association. 2022 July; 117 (539):1563-1578
A General Framework for Quantile Estimation with Incomplete Data
Han, P., Kong, L., Zhao, J., and Zhou, X.
Journal of Royal Statistical Society: Series B. 2019 October; 81 (2):305-333
Model-Robust Designs for Quantile Regression
Kong, L. and Wiens, D.
Journal of the American Statistical Association. 2015 January; 110 (507):233-245
Multivariate Varying Coefficient Models for Functional Responses
Zhu, H., Li, R., and Kong, L.
Annals of Statistics. 2014 June; 40 (5):2634-2666
Spatially Varying Coefficient Model for Neuroimaging Data with Jump Discontinuities
Zhu, H., Fan, J., and Kong, L.
Journal of the American Statistical Association. 2012 August; 109 (507):1084-1098
Discussion of "Multivariate Quantiles and Multiple- Output Regression Quantiles: From L1 Optimization to Halfspace Depth"
Kong, L. and Mizera, I.
Annals of Statistics. 2010 November; 38 (2):685-693