Lei (Larry) Hua, ASA, PhD (UBC 2012)
Division of Statistics
Department of Mathematical sciences
Northern Illinois University
DeKalb, IL, 60115, United States
Multivariate dependence modeling / Multivariate non-Gaussian models / Copulas / Extreme value theory / Quantitative risk management / Actuarial theory and applications / Quantitative-Statistical-Computational finance
Aug 2012 - now, Tenure-track Assistant Professor, Division of Statistics, Northern Illinois University
The 50th Actuarial Research Conference, Aug. 2015, contributed talk: Factor Copula Approaches for Assessing Spatially Dependent High-dimensional Risks
Department Colloquium at Illinois State University, October, 2014, invited talk: Tail negative dependence and its applications for aggregate loss modeling
The 18th International Congress on Insurance: Mathematics and Economics, July, 2014, contributed talk: Factor copulas and beyond.
Department Colloquium at University of Wisconsin at Milwaukee, April, 2014, invited talk: Relations Between Hidden Regular Variation and Tail Order of Copulas.
International Workshop on High-Dimensional Dependence and Copulas: Theory, Modeling, and Applications, Jan. 2014, invited talk: Tail order and its applications.
Department Colloquium at McGill University, Nov. 2013, invited talk: Tail order and its applications.
The 48th Actuarial Research Conference, Aug. 2013, contributed talk: Assessing high-risk scenarios by full-range tail dependence copula.
Department Colloquium at University of Science and Technology of China, Jul. 2013, invited talks: Seeking new copulas through tail orders, Strength of tail dependence based on conditional tail expectation.
The 8th Conference on Extreme Value Analysis, Jul. 2013, contributed talk: Relations between hidden regular variation and tail order of copulas.
Banff workshop on Non-Gaussian Multivariate Statistical Models and their Applications, May 2013, invited talk: Strength of Tail Dependence based on Conditional Tail Expectation.
Selected Grants / Awards
Co-investigator: “Cybersecurity Insurance: Modeling and Pricing”, with Maochao Xu, funded by Society of Actuaries, 2015-2016
Principal Investigator: “Factor copula approaches for assessing spatially dependent high-dimensional risks”, with Sanjib Basu, Michelle Xia, Individual Grant Competition (SOA/CAS), 2014
Sole Investigator: “Tail Negative Dependence and Its Applications for Aggregate Loss Modeling”, Individual Grant Competition (CAS), 2013
Sole Investigator: “Assessing High-Risk Scenarios by Full-Range Tail Dependence Copula”, CAS/CIA/SOA Joint Risk Management Section, 2013
Sole Investigator: "Multivariate extreme dependence and risk measures", Alexander Graham Bell CGS D-3, 2009 - 2012
Li, H. and Hua, L., 2015. Higher order tail densities of copulas and hidden regular variation, Journal of Multivariate Analysis 138, 143-155. [pdf]
Hua, L., Joe, H. and Li, H., 2014. Relations between hidden regular variation and tail order of copulas, Journal of Applied Probability 51(1), 37-57. [pdf]
Hua, L. and Joe, H., 2014. Strength of tail dependence based on conditional tail expectation, Journal of Multivariate Analysis 123, 143-159. [pdf]
Mao, T. and Hua, L., 2016. Second-order regular variation inherited from Laplace-Stieltjes transforms, Communications in Statistics - Theory and Methods 45(15), 4569-4588. [pdf]
Hua, L. and Xia, M., 2014. Assessing high-risk scenarios by full-range tail dependence copula, North American Actuarial Journal, 18(3), 363-378. [pdf]
Ebrahimi, N. and Hua, L., 2014. Assessing the reliability of a nanocomponent by using copulas, IIE Transactions 46(11), 1196-1208. [pdf]
Hua, L. and Joe, H., 2012. Tail comonotonicity: properties, constructions, and asymptotic additivity of risk measures, Insurance Mathematics and Economics 51, 492-503. [pdf]
Hua, L. and Joe, H., 2012. Tail comonotonicity and conservative risk measures, ASTIN Bulletin 42(2), 601-629. [pdf]
Hua, L. and Joe, H., 2011. Second order regular variation and conditional tail expectation of multiple risks, Insurance Mathematics and Economics 49, 537-546. [pdf]
Hua, L. and Joe, H., 2011. Tail order and intermediate tail dependence of multivariate copulas, Journal of Multivariate Analysis 102(10), 1454-1471. [pdf]
Hua, L. and Cheung, K.C., 2008. Stochastic orders of scalar products with applications, Insurance: Mathematics and Economics 42, 865-872. [pdf]
Hua, L. and Cheung, K.C., 2008. Worst allocations of policy limits and deductibles, Insurance: Mathematics and Economics 43, 93-98. [pdf]
Hua, L. and Joe, H. 2013. Intermediate tail dependence: a review and some new results, Stochastic Orders in Reliability and Risks (editors: Li, H. and Li, X.): In Honor of Professor Moshe Shaked, Lecture Notes in Statistics, Springer, 291-311. [pdf]
A vine copula model for predicting the eﬀectiveness of cyber defense early-warning, with Maochao Xu, Shouhuai Xu
Factor copula approaches for assessing spatially dependent high-dimensional risks, with Michelle Xia, Sanjib Basu
Assessing component reliability using lifetime data from systems, with Nader Ebrahimi, Michelle Xia
Multivariate dependence modeling based on comonotonic factors, with Harry Joe
On a bivariate copula with both upper and lower full-range tail dependence
(Working papers are avaliable upon request!)
Review about 20 papers per year for following journals:
Journal of Multivariate Analysis / Biometrika / Statistics and Computing / Insurance: Mathematics & Economics / Journal of the Royal Statistical Society: Series B / North American Actuarial Journal / Scandinavian Actuarial Journal / Extremes / Journal of Banking and Finance / Computational Statistics and Data Analysis / Scandinavian Journal of Statistics / The American Statistician / Communications in Statistics / Journal of Mathematical Analysis and Applications / Journal of Biopharmaceutical Statistics / Statistics and Its Interface / Annals of Operations Research / Statistical Methods in Medical Research / Lifetime Data Analysis / Advances and Applications in Statistics / Journal of Quality Technology & Quantitative Management / Methodology and Computing in Applied Probability / SIAM Journal on Financial Mathematics
Editorial Advisory Board of Dependence Modeling