In the past 25 years, computer scientists and statisticians developed machine learning
algorithms capable of modeling highly nonlinear transformations and interactions of input …

This paper focuses on the estimation and application aspects of the Erlang count logit-
weighted reduced mixture of experts model (EC-LRMoE), which is a fully flexible multivariate …

This paper is concerned with introducing a family of multivariate mixed Negative Binomial
regression models in the context of a posteriori ratemaking. The multivariate mixed Negative …

The logit-weighted reduced mixture of experts model (LRMoE) is a flexible yet analytically
tractable non-linear regression model. Though it has shown usefulness in modeling …

We discuss how mixtures of Gamma distributions with mixing probabilities, shape and rate
parameters depending on features can be fitted with neural networks. We develop two …

Insurance loss severity data often exhibit heavy-tailed behavior, complex distributional
characteristics such as multimodality, and peculiar links between policyholders' risk profiles …

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 …

Recently, Heras et al.(2018. An application of two-stage quantile regression to insurance
ratemaking. Scandinavian Actuarial Journal 9, 753–769) propose a two-step inference to …

In property insurance claims triage, insurers often use static information to assess the
severity of a claim and to identify the subsequent actions. We hypothesize that the pattern of …

To facilitate applications in general insurance, some extensions are proposed to cluster-
weighted models (CWMs). First, we extend CWMs to have generalized cluster-weighted …