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

Traditional techniques for calculating outstanding claim liabilities such asthe chain ladder are notoriously at risk of being distorted by outliers in pastclaims data. Unfortunately, the literature in robust methods of reserving isscant, with notable exceptions such as Verdonck and Debruyne (2011) andVerdonck and Van Wouwe (2011). In this paper, we put forward two alternativerobust bivariate chain-ladder techniques to extend the approach of Verdonck andVan Wouwe (2011). The first technique is based on Adjusted Outlyingness (Hubertand Van der Veeken, 2008) and explicitly incorporates skewness into theanalysis whilst providing a unique measure of outlyingness for eachobservation. The second technique is based on bagdistance (Hubert et al., 2016)which is derived from the bagplot however is able to provide a unique measureof outlyingness and a means to adjust outlying observations based on thismeasure.  Furthermore, we extend our robust bivariate chain-ladder approach to anN-dimensional framework. The implementation of the methods, especially beyondbivariate, is not trivial. This is illustrated on a trivariate data set fromAustralian general insurers, and results under the different outlier detectionand treatment mechanisms are compared.

Detection and treatment of outliers for multivariate robust loss reserving