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LRMoE.jl: a software package for insurance loss modelling using mixture of experts regression model

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  • Tseung, Spark C.
  • Badescu, Andrei L.
  • Fung, Tsz Chai
  • Lin, X. Sheldon

Abstract

This paper introduces a new julia package, LRMoE, a statistical software tailor-made for actuarial applications, which allows actuarial researchers and practitioners to model and analyse insurance loss frequencies and severities using the Logit-weighted Reduced Mixture-of-Experts (LRMoE) model. LRMoE offers several new distinctive features which are motivated by various actuarial applications and mostly cannot be achieved using existing packages for mixture models. Key features include a wider coverage on frequency and severity distributions and their zero inflation, the flexibility to vary classes of distributions across components, parameter estimation under data censoring and truncation and a collection of insurance ratemaking and reserving functions. The package also provides several model evaluation and visualisation functions to help users easily analyse the performance of the fitted model and interpret the model in insurance contexts.

Suggested Citation

  • Tseung, Spark C. & Badescu, Andrei L. & Fung, Tsz Chai & Lin, X. Sheldon, 2021. "LRMoE.jl: a software package for insurance loss modelling using mixture of experts regression model," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 419-440, July.
    • Handle: RePEc:cup:anacsi:v:15:y:2021:i:2:p:419-440_11
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    Cited by:

    1. Sebastian Calcetero-Vanegas & Andrei L. Badescu & X. Sheldon Lin, 2023. "Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method," Papers 2307.10808, arXiv.org, revised Jul 2023.

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