Phase-type mixture-of-experts regression for loss severities

@article{Bladt2022PhasetypeMR,
  title={Phase-type mixture-of-experts regression for loss severities},
  author={Martin Bladt and Jorge Yslas},
  journal={Scandinavian Actuarial Journal},
  year={2022}
}
. The task of modeling claim severities is addressed when data is not consistent with the classical regression assumptions. This framework is common in several lines of business within insurance and reinsurance, where catastrophic losses or heterogeneous sub-populations result in data difficult to model. Their correct analysis is required for pricing insurance products, and some of the most prevalent recent specifications in this direction are mixture-of-experts models. This paper proposes a… 

Figures and Tables from this paper

Joint discrete and continuous matrix distribution modelling

. In this paper we introduce a bivariate distribution on R + × N arising from a single underlying Markov jump process. The marginal distributions are phase-type and discrete phase-type distributed,

JOINT LIFETIME MODELLING WITH MATRIX

. Acyclic phase-type (PH) distributions have been a popular tool in survival analysis, thanks to their natural interpretation in terms of ageing towards its inevitable absorption. In this paper, we

Joint lifetime modelling with matrix distributions

. Acyclic phase-type (PH) distributions have been a popular tool in survival analysis, thanks to their natural interpretation in terms of ageing towards its inevitable absorption. In this paper, we

References

SHOWING 1-10 OF 32 REFERENCES

PHASE-TYPE DISTRIBUTIONS FOR CLAIM SEVERITY REGRESSION MODELING

This paper proposes to use regression models based on phase-type distributions, regressing on their underlying inhomogeneous Markov intensity and using an extension of the expectation–maximization algorithm to provide flexible regression models that effectively capture the entire distribution of loss severities.

A New Class of Severity Regression Models with an Application to IBNR Prediction

The proposed TG-LRMoE model is applied to fit the severity and reporting delay components of a European automobile insurance dataset and is shown to be useful and crucial for adequate prediction of incurred but not reported (IBNR) reserves through out-of-sample testing.

A Class of Mixture of Experts Models for General Insurance: Theoretical Developments

This work can heuristically interpret the LRMoE as a “fully flexible” model to capture any distributional, dependence and regression structures subject to a denseness condition and the mathematical tractability of the LR MoE is guaranteed since it satisfies various marginalization and moment properties.

Modeling loss data using mixtures of distributions

Mortality modeling and regression with matrix distributions

LRMoE: An R Package for Flexible Actuarial Loss Modelling Using Mixture of Experts Regression Model

A new R package 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.

Mixture composite regression models with multi-type feature selection

This paper presents a group-fused regularization approach that allows for selecting the explanatory variables which significantly impact the mixing probabilities and the different mixture components, respectively, and develops an asymptotic theory for this regularized estimation approach.

Modeling and Evaluating Insurance Losses Via Mixtures of Erlang Distributions

Abstract In this paper we suggest the use of mixtures of Erlang distributions with common scale parameter to model insurance losses. A modified expectation-maximization (EM) algorithm for parameter

Fitting inhomogeneous phase‐type distributions to data: the univariate and the multivariate case

The class of inhomogeneous phase‐type distributions (IPH) was recently introduced in Albrecher & Bladt (2019) as an extension of the classical phase‐type (PH) distributions. Like PH distributions,

Inhomogeneous Markov Survival Regression Models

We propose new regression models in survival analysis based on homogeneous and inhomogeneous phase-type distributions. The intensity function in this setting plays the role of the hazard function.