English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With insurers benefiting from ever-larger amounts of data of increasingcomplexity, we explore a data-driven method to model dependence withinmultilevel claims in this paper. More specifically, we start from anon-parametric estimator for Archimedean copula generators introduced by Genestand Rivest (1993), and we extend it to diverse flexible censoring scenariosusing techniques derived from survival analysis. We implement a graphicalselection procedure for copulas that we validate using goodness-of-fit methodsapplied to complete, single-censored, and double-censored bivariate data. Weillustrate the performance of our model with multiple simulation studies. Wethen apply our methodology to a recent Canadian automobile insurance datasetwhere we seek to model the dependence between the activation delays ofcorrelated coverages. We show that our model performs quite well in selectingthe best-fitted copula for the data at hand, especially when the dataset islarge, and that the results can then be used as part of a larger claimsreserving methodology.

A non-parametric estimator for Archimedean copulas under flexible censoring scenarios and an application to claims reserving