LRMoE

R package LRMoE: LRMoE is a package tailor-made for actuarial applications which allows actuarial researchers and practitioners to model and analyze insurance loss frequencies and severities using the Logit-weighted Reduced Mixture-of-Experts (LRMoE) model. The flexibility of LRMoE models is theoretically justified in Fung et al. (2019), and an application of LRMoE for modelling correlated insurance claim frequencies is in Fung et al. (2019).

Keywords for this software

bodily injury and property damage MTPL claim counts
rate-making
nonlife insurance
severity regression model
regression models
IBNR prediction
multivariate mixed negative binomial regression model
expectation-maximization algorithm
EM algorithm
a posteriori ratemaking
mixtures of distributions
neural networks

References in zbMATH (referenced in 3 articles )

Showing results 1 to 3 of 3.

Sorted by year (citations)

Delong, Łukasz; Lindholm, Mathias; Wüthrich, Mario V.: Gamma mixture density networks and their application to modelling insurance claim amounts (2021)
Fung, Tsz Chai; Badescu, Andrei L.; Lin, X. Sheldon: A new class of severity regression models with an application to IBNR prediction (2021)
Tzougas, George; Pignatelli di Cerchiara, Alice: The multivariate mixed negative binomial regression model with an application to insurance a posteriori ratemaking (2021)