Research

Peer Reviewed /Refereed Publications

Published

Journal Article

1. Xiong, L., Luo, J.; Vise, H.; White, M. (2023). Distributed Least-Squares Monte Carlo for American Option Pricing. Risks, 11(8), 145. https://doi.org/10.3390/risks11080145

2. Xiong, L., Manathunga, V., Luo, J., Dennison, N., Zhang, R., Xiang, Z.  (2023). AutoReserve: a web-based tool for personal auto insurance loss reserving with classical and machine learning methods. Risks, 11(7), 131. https://doi.org/10.3390/risks11070131

3. Liu, Y., Yang, L., Xiong, L., Performance Commitments and the Properties of Analyst Earnings Forecasts: Evidence from Chinese Reverse Merger Firms. International Review of Financial Analysis. https://authors.elsevier.com/c/1hOl-3mS~2a18U

4. Xiong, L., Tian, K., Qian, Y., Musyoka, W., Chen, X. (2023). Determine the Undervalued US Major League Baseball Players with Machine Learning. International Journal of Innovative Technology and Exploring Engineering. https://www.ijitee.org/portfolio-item/B94060112223/

5. Xiong, L., Hong, D. (2022). CapSolve: A Solvency Assessment and Prediction Framework for Workers’ Compensation Captive Insurance Companies. Journal of Insurance Issues, 45(2), 82-113. https://www.jstor.org/stable/48703228 

6. Xiong, L., Williams, S. D. (2022). Generalized Linear Model for Predicting the Credit Card Default Payment Risk. Advances in Science, Technology and Engineering Systems Journal (Special Issue on Innovation in Computing, Engineering Science & Technology). https://doi.org/10.25046/aj070306 

7. Xiong, L. (2022). Predictive Modeling for Transportation Security Administration Claims Data. ANWESH: International Journal of Management & Information Technology, 7(2), 10-20. http://www.publishingindia.com/anwesh/106/predictive-modelling-for-transportation-security-administration-claims-data/32006/76746/ 

8. Xiong, L., Sun, T., Green, R. (2021). Predictive Analytics for 30-day Hospital Readmissions. Mathematical Foundations of Computing, Online First. https://www.aimsciences.org/article/doi/10.3934/mfc.2021035

9. Xiong, L., Hong, D. (2014). Multi-resolution Analysis Method for IMS Data Biomarker Selection and Classification. British Journal of Mathematics and Computer Science, 5(1), 65-81. https://doi.org/10.9734/BJMCS/2015/9870

Conference Proceeding

10. Xiong, L. (2020). Comparative Study of Predictive Analytics Algorithms and Tools on Property and Casualty Insurance Solvency Prediction (pp. 81-88). Association for Computing Machinery (ACM). https://doi.org/10.1145/3418653.3418663

11. Xiong, L., Hong, D. (2020). Using Monte Carlo Simulation to Predict Captive Insurance Solvency (pp. 84- 88). Association for Computing Machinery (ACM). https://doi.org/10.1145/3388142.3388171

Book Chapter

12. Xiong, L., Hong, D. (2017). An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Proteomic Data Processing. Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Murfreesboro: Springer International Publishing. https://www.springerprofessional.de/en/an-mcmc-mrf-algorithm-for-incorporating-spatial-information-in-i/11933362

Accepted

Journal Article

13. Xiong, L., Liang, J., Chen, X., Cao, X., Zhu, P., Zhao, M. (2023). Tree-based Machine Learning Methods for Analytics of Online Shoppers’ Purchasing Intentions. International Journal of Data Science.

Non-Peer Reviewed/Refereed Publications 

Published

Other

• Xiong, L. (2014). Statistical Computing Schemes for Proteomics Data Processing and Insurance Solvency Modeling. http://jewlscholar.mtsu.edu/handle/mtsu/4331

Research Currently in Progress

• "Fast Gauss Transform and its improvement" (On-Going). (September 2020 - Present).

• "Insurance Data and Natural Language Processing" (On-Going). (October 2022 - Present).

The research project's purpose is to develop a framework for applying natural language processing (NLP) techniques on the Insurance data. For this direction, we are exploring the worker compensation data set which contains textual description and apply several popular NLP techniques such as BERT coupled with other deep learning techniques to identify certain factors like number of days the claim is active.

• "Healthcare data integration based on HL7 technology" (On-Going). (January 2021 - Present).

Healthcare data are constantly generated from different devices, hardware, and platforms with different data formats. How to integrate these data into a compatible, consistent format and deliver to the correct recipients? How to present these data on different platforms like IOS, Android App? What insights we can have from the data by using analytics? These are the problems this research is trying to answer.

• "Machine learning for proxy model in actuarial science" (On-Going). (September 22, 2020 - Present).

The traditional proxy model uses polynomials such as regression equation to reduce the complexity of sophisticated models. We are interested to apply the machine learning algorithms such as deep learning to train an approximated model that performs better than the polynomial models.

• "Automated Machine Learning with the actuarial application" (On-Going). (April 2019 - Present).