Research
(* indicates a student co-author)
Working papers
On the nonexistence of a genuinely optimal solution to the search problem, https://arxiv.org/abs/2311.18226
On the optimal civilian response during active shooter incidents, https://arxiv.org/abs/2310.17111
A new approach to detecting insurance fraud (with H. Yang*)
Two pitfalls in the estimation of lateral range curves
List of Publications
53. Hong, L.(2024). On several properties of uniformly optimal search plans. Military Operations Research, to appear.
52. Xu, X.* and Hong, L. (2024). Instantaneous and limiting behavior of an n-node blockchain under cyber attacks from a single hacker. Business Analytics in Practice: Enhancing Decision Making (Edited by Emrouznejad et al.), Lecture Notes in Operations Research, Springer, New York, to appear.
51. Hong, L.(2024). Reply to Discussion on ``Sample size determination on credibility estimation'', North American Actuarial Journal, to appear.
50. Hong, L.(2023). Conformal prediction credibility intervals. North American Actuarial Journal, 27(4), 675-688.
49. Hong, L. (2023). Infinitely many new derivations of the geometric expectation. Mathematics Magazine, 96(3), 347-348.
48. Hong, L., Schlesinger, H. and Zhuang, B. (2023). Whoops! It happened again. Demand for insurance that covers multiple risks, Risks, 11(4), 73.
47. Hong, L. (2022). A sample size determination for credibility estimation. North American Actuarial Journal 26, 485-495.
46. Hong, L. and Martin, R. (2022). Imprecise credibility theory, Annals of Actuarial Science 16, 135-150.
45. Jun. H. and Hong, L. (2022). A nonparametric sequential learning procedure for estimating the pure premium. European Actuarial Journal 12, 485-502.
44. Hong, L. and Martin, R. (2021). Valid model-free prediction of future insurance claims. North American Actuarial Journal 25(4), 473--483.
43. Hong, L., Yan, Z. and Zhuang, B. (2021). Optimal insurance coverage with default risk. Journal of Insurance Issues 44(2) 31-44.
42. Hong, L. and Martin, R. (2021). Discussion on "q-Credibility" by Olivier Le Courtois. Variance: Journal of the Casualty Actuarial Society 14(2), 1-4.
41. Hong, L. and Martin, R. (2020). Model misspecification, Bayesian versus credibility estimation, and Gibbs posteriors. Scandinavian Actuarial Journal 2020(7), 634-649.
40. Hong, L. (2020). On three standard results in the theory of insurance demand. Asia-Pacific Journal of Risk and Insurance 14(1), 1-10.
39. Hong, L. (2020). Continuous counterexamples for three dependence notions. Communications in Statistics-Theory and Methods 49(19), 4853-4858.
38. Hong, L. (2019). Remarks on the Mossin theorem. North American Actuarial Journal 23(1), 1-10.
37. Syring, N., Hong, L. and Martin, R (2019). Gibbs posterior inference on value-at-risk. Scandinavian Actuarial Journal 2019(7), 548-557.
36. Hong, L. and Martin, R (2019). Real-time Bayesian nonparametric prediction of solvency risk. Annals of Actuarial Science 13, 67-79.
35. Hong, L. (2019). On the properties of the primary loss and the excess loss in NCCI's experience rating plan. Variance: Journal of the Casualty Actuarial Society 12(2), 125-141.
34. Hong, L. (2019). A note on the relationship between three classes of operators on Riesz spaces. Analele Universatatii de Vest din Timisoara: Seria Matematica-Informatica~LVII~(2), 1--9.
33. Hong, L. (2018). A note on Mossin's Theorem for deductible insurance under random initial wealth. Scandinavian Actuarial Journal 5, 404-411.
32. Hong, L. and Martin, R. (2018). Dirichlet process mixture models for insurance loss data. Scandinavian Actuarial Journal 6, 545-554.
31. Hong, L., Kuffner, T. and Martin, R. (2018). On overfitting and post-selection uncertainty assessments. Biometrika 105(1), 221-224.
30. Hong, L., Kuffner, T. and Martin, R. (2018). On prediction of future insurance claims when the model is uncertainty. Variance: Journal of the Casualty Actuarial Society 12(1), 90-99.
29. Hong, L. (2018). A further study on the choice between two hedging strategies-the continuous case. Methodology and Computing in Applied Probability 20, 1189-1198.
28. Hong, L. and Martin, R. (2017). A flexible Bayesian nonparametric model for predicting future insurance claims. North American Actuarial Journal 21(2), 228-241.
27. Hong, L. and Martin, R. (2017). A review of Bayesian asymptotics in general insurance applications. European Actuarial Journal 7(1), 231-255.
26. Hong, L. (2017). Estimators of contingent probabilities and means with actuarial applications. Communications in Statistics-Theory and Methods 46(2), 927-941.
25. Hong, L. (2016). On the choice between two delta-hedging strategies. Decisions in Economics and Finance 39(1), 69–80.
24. Hong, L. and Martin, R. (2016). Discussion on "Credibility Estimation of Distribution Functions with Applications to Experience Rating in General Insurance". North American Actuarial Journal 20(1), 95-98.
23. Hong, L. (2016). Discussion: The mathematics of excess loss. Variance: Journal of the Casualty Actuarial Society 9(1), 1-3.
22. Hong, L. (2016). On order-bounded subsets of locally solid Riesz spaces. Quaestiones Mathematicae 39(3), 381–389.
21. Hong, L. (2015). Ando-Douglas type characterization of optional projections and predictable projections. Indagationes Mathematicae: Journal of Mathematical Sciences for the Royal Dutch Mathematical Society 26, 413--420.
20. Hong, L. and Zou, J. (2015). Jump tests for semimartingales. South African Actuarial Journal 15, 93-108.
19. Hong, L. (2015). Another remark on the alternative expectation formula. The American Statistician 69(3), 157–159.
18. Hong, L. (2015). Locally solid topological lattice-ordered groups. Archivum Mathematicum 51, 107-128.
17. Hong, L. (2015). The absolute difference law for expectations. The American Statistician 69(1), 8-10.
16. Hong, L. (2015). Fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections. Analele Universitatii de Vest din Timisoara: Seria Matematica - Informatica 53(1), 77-108.
15. Hong, L. (2014). Two new elementary derivations of the expectation of geometric distribution. The American Statistician 68(3), 188-192.
14. Yan, Z. and Hong, L. (2014). Testing asymmetric information in reinsurance market. Geneva Papers on Risk and Insurance: Issues and Practice 40, 29-46.
13. Hong, L. (2014). Letter to the Editor: The Mean Value Theorem and Taylor's Expansion in Statistics. The American Statistician 68(3) , 220.
12. Hong, L. (2014). The linear topology associated with weak convergence of probability measures. Missouri Journal of Mathematical Sciences 26(2), 168-172.
11. Hong, L. (2013). Some remarks on capital allocation by percentile layer. European Actuarial Journal 3(2), 439-452.
10. Hong, L. and Sarkar, J. (2013). Contingent means in multi-life models. Scandinavian Actuarial Journal 5, 340-351.
9. Hong, L. (2012). A remark on the alternative expectation formula. The American Statistician 66(4), 232-233.
8. Elshahat, A., Parhizgar, A. and Hong, L. (2012). The information content of the banking regulatory agencies and the depository credit intermediation institutions. Journal of Economics and Business 64, 90-104.
7. Bieth, B., Hong, L. and Sarkar, J. (2011). Limiting availability of a one-unit system backed by a spare under repair or preventive maintenance. Missouri Journal of Mathematical Sciences 23(1), 75--87.
6. Hong, L. (2011). Exchange Rates. Encyclopedia of Mathematics and Society. Salem Press, Pasadena, CA, USA.
5. Hong, L. (2011). Bankruptcy, Business, Encyclopedia of Mathematics and Society. Salem Press, Pasadena, CA, USA.
4. Hong, L. (2011). Bankruptcy, Personal. Encyclopedia of Mathematics and Society. Salem Press, Pasadena, CA, USA.
3. Hong, L. and Elshahat, A. (2010). Conditional tail variance and conditional tail skewness in finance and insurance. Journal of Financial and Economic Practice 10(1), 147-156.
2. Bieth, B., Hong, L. and Sarkar, J., (2010). A standby system with two repair persons under arbitrary life- and repair times. Mathematical and Computer Modeling 51(5-6), pp. 756-767.
1. Bieth, B., Hong, L. and Sarkar, J., (2010). A standby system with two types of repair persons. Applied Stochastic Models in Business and Industry 26(5), 577-594.