Quotation Fissler, Tobias. 2022. Deep Quantile And Deep Composite Model Regression. Statistical Society of Canada 2022 Annual Meeting, Vancouver, Canada, 30.05.–02.06.


RIS


BibTeX

Abstract

A main difficulty in actuarial claim size modeling is that there is no simple off-the-shelf distribution that simultaneously provides a good distributional model for the main body and the tail of the data. In particular, covariates may have different effects for small and for large claim sizes. To cope with this problem, we introduce a deep composite regression model whose splicing point is given in terms of a quantile of the conditional claim size distribution rather than a constant. To facilitate M-estimation for such models, we introduce and characterize the class of strictly consistent scoring functions for the triplet consisting a quantile, as well as the lower and upper expected shortfall beyond that quantile. In a second step, this elicitability result is applied to fit deep neural network regression models. We demonstrate the applicability of our approach and its superiority over classical approaches on a real accident insurance data set.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Deep Quantile And Deep Composite Model Regression
Event Statistical Society of Canada 2022 Annual Meeting
Year 2022
Date 30.05.¿02.06.
Country Canada
Location Vancouver
URL https://ssc.ca/en/meetings/annual/2022-annual-meeting

Associations

People
Fissler, Tobias (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search