Isotonic recalibration under a low signal-to-noise ratio

被引：3

作者：

Wuthrich, Mario V. ^{[1
]}

Ziegel, Johanna ^{[2
]}

机构：

[1] Swiss Fed Inst Technol, Dept Math, RiskLab, Zurich, Switzerland

[2] Univ Bern, Inst Math Stat & Actuarial Sci, Bern, Switzerland

来源：

SCANDINAVIAN ACTUARIAL JOURNAL | 2024年 / 2024卷 / 03期

关键词：

Auto-calibration; isotonic regression; isotonic recalibration; low signal-to-noise ratio; cross-financing; algorithmic solution; deep neural network; explainability; SET;

D O I：

10.1080/03461238.2023.2246743

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Insurance pricing systems should fulfill the auto-calibration property to ensure that there is no systematic cross-financing between different price cohorts. Often, regression models are not auto-calibrated. We propose to apply isotonic recalibration to a given regression model to restore auto-calibration. Our main result proves that under a low signal-to-noise ratio, this isotonic recalibration step leads to an explainable pricing system because the resulting isotonically recalibrated regression function has a low complexity.

引用

页码：279 / 299

页数：21

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