报告时间：2022年4月6日

               下午15:00-16:00

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https://ethz.zoom.us/j/63161303422

Meeting ID: 631 6130 3422）

报告嘉宾：Mario Wüthrich

报告主题：Deep Composite Regression

报告摘要

Deep Composite Regression

A main difficulty in actuarial claim size modeling is that there often 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 on small and large claim sizes. To cope with this problem, we discuss 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 in such models, we consider and characterize the class of strictly consistent scoring functions for the triplet consisting of the 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.

个人简介

Mario Wüthrich is Professor in the Department of Mathematics at ETH Zurich and Honorary Visiting Professor at Bayes Business School (2011-2022). He holds a Ph.D. in Mathematics from ETH Zurich (1999). From 2000 to 2005, he held an actuarial position at Winterthur Insurance, Switzerland. He is Actuary SAA (2004), served on the board of the Swiss Association of Actuaries (2006-2018), and is Editor-in-Chief of ASTIN Bulletin (since 2018).