在贝叶斯概括的极端值混合自回归模型上,在非标准的保险学数据中调整了SNR
Chrisandi R Lande1,2, Nur Iriawan1, Dedy Dwi Prastyo1
1Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.
MethodsX
|January 10, 2025
概括
新的通用极端价值组合自动回归 (GEVMAR) 模型有效地分析复杂的保险索赔储备数据. 它的性能优于标准模型,为波动性和多模式金融数据集提供了更高的准确性.
科学领域:
- 精算科学是一种精算科学.
- 统计建模 统计建模
- 风险管理 风险管理
背景情况:
- 标准模型与波动性和多式联运保险索赔储备数据作斗争.
- 现有的自回归和高斯混合自回归 (GMAR) 模型在捕获复杂数据动态方面存在局限性.
研究的目的:
- 为非标准的保险学数据集引入通用极端值组合自回归 (GEVMAR) 模型.
- 使用修改的信号与噪声比 (SNR) 度量来提高保险赔偿准备金的预测准确度.
- 解决当前模型在处理极端波动和多式联运分布方面的局限性.
主要方法:
- 开发了通用化极端值混合自回归 (GEVMAR) 模型,将通用化极端值 (GEV) 分布与贝叶斯估计集成在一起.
- 应用了修改后的信号噪声比 (SNR) 度量,以提高预测准确度.
- 利用贝叶斯式技术,提高了分析重尾数据集的多功能性.
主要成果:
- 在印度尼西亚保险索赔储备数据 (2015-2023) 中,GEVMAR模型 (GEV I型) 显示出优异的性能.
- 与GMAR模型 (MAPE 7.5812) 相比,实现了改进的调整SNR指标 (1.3894 × 106) 和降低了0.0189的平均绝对百分比误差 (MAPE).
- 贝叶斯式方法促进了对具有极端可变性的数据集的有效分析.
结论:
- GEVMAR模型为分析复杂的,非标准的精算数据,特别是索赔储备提供了显著的进步.
- 它克服了传统模型在管理数据波动和多式联运模式方面的局限性.
- 拟议的方法提供了一个更准确和多功能工具,用于精算风险评估.
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