In this project, we develop software packages in Julia and R which implement the Logit-weighted Reduced Mixture-of-Experts model, a flexible framework for insurance loss modelling.
Badescu, A.L., Fung, T.C., Lin, X.S. and Tseung, S.C. 2021. A better fit: presenting an intuitive and flexible non-linear regression model, The Actuary, pp.24-26. (Magazine article, not peer-reviewed)
Tseung, S.C., Badescu, A.L., Fung, T.C. and Lin, X.S., 2021. LRMoE. jl: a software package for insurance loss modelling using mixture of experts regression model. Annals of Actuarial Science, pp.1-22.
Tseung, S.C., Badescu, A., Fung, T.C. and Lin, X.S., 2020. LRMoE: an R package for flexible actuarial loss modelling using mixture of experts regression model. Available at SSRN 3740215.
Presentation: Actuarial Research Conference, 2020
Applied Data Scientist, Lydia.ai (Aug 2021 – Present):
Presentation: Opening the Machine Learning Black Box in Regulated Industries, 5th Annual Toronto Machine Learning Summit, 2021 (with Hanieh Arjmand)
All / Julia / Python / R / Tools / Random
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