Show simple item record

dc.contributor.authorPočučaa, Nikola
dc.contributor.authorJevtićb, Petar
dc.contributor.authorMcNicholasa, Paul
dc.contributor.authorMiljkovicc, Tatjana
dc.description.abstractIn this paper, we propose two important extensions to cluster-weighted models (CWMs). First, we extend CWMs to have generalized cluster-weighted models (GCWMs) by allowing modeling of non-Gaussian distribution of the continuous covariates, as they frequently occur in insurance practice. Secondly, we introduce a zero-inflated extension of GCWM (ZI-GCWM) for modeling insurance claims data with excess zeros coming from heterogenous sources. Additionally, we give two expectation-optimization (EM) algorithms for parameter estimation given the proposed models. An appropriate simulation study shows that, for various settings and in contrast to the existing mixture-based approaches, both extended models perform well. Finally, a real data set based on French auto-mobile policies is used to illustrate the application of the proposed extensions.en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.titleModeling Frequency and Severity of Claims with the Zero-Inflated Generalized Cluster-Weighted Modelsen_US
dc.typeJournal Articleen_US

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States