Linglong Kong, PhD

Associate Professor, Faculty of Science - Mathematics & Statistical Sciences

Contact

Associate Professor, Faculty of Science - Mathematics & Statistical Sciences
Email
lkong@ualberta.ca

Overview

Research

Research Areas

Functional and Neuroimaging data analysis, Robust statistics and quantile regression, and Statistical machine learning.

Courses

STAT 512 - Techniques of Mathematics for Statistics

Introduction to mathematical techniques commonly used in theoretical Statistics, with applications. Applications of diagonalization results for real symmetric matrices, and of continuity, differentiation, Riemann-Stieltjes integration and multivariable calculus to the theory of Statistics including least squares estimation, generating functions, distribution theory. Prerequisite: consent of Department.

Fall Term 2020
STAT 562 - Discrete Data Analysis

Sampling models and methods of inference for discrete data. Maximum likelihood estimation for complete contingency tables, measures of association and agreement. Goodness-of-fit. Incomplete tables. Analysis of square tables; symmetry and marginal homogeneity. Model selection and closeness of fit; practical aspects. Chi-square tests for categorical data from complex surveys. Prerequisite: STAT 372 or 471.

Fall Term 2020

Browse more courses taught by Linglong Kong

Publications

Discussion of "Multivariate Quantiles and Multiple- Output Regression Quantiles: From L1 Optimization to Halfspace Depth"
Author(s): Kong, L. and Mizera, I.
Publication: Annals of Statistics
Volume: 38
Issue: 2
Page Numbers: 685-693

Multivariate Varying Coefficient Models for Functional Responses
Author(s): Zhu, H., Li, R., and Kong, L.
Publication: Annals of Statistics
Volume: 40
Issue: 5
Page Numbers: 2634-2666

Spatially Varying Coefficient Model for Neuroimaging Data with Jump Discontinuities
Author(s): Zhu, H., Fan, J., and Kong, L.
Publication: Journal of the American Statistical Association
Volume: 109
Issue: 507
Page Numbers: 1084-1098

Model-Robust Designs for Quantile Regression
Author(s): Kong, L. and Wiens, D.
Publication: Journal of the American Statistical Association
Volume: 110
Issue: 507
Page Numbers: 233-245

A General Framework for Quantile Estimation with Incomplete Data
Author(s): Han, P., Kong, L., Zhao, J., and Zhou, X.
Publication: Journal of Royal Statistical Society: Series B
Volume: 81
Issue: 2
Page Numbers: 305-333