统一的死亡率预测模型:对GAS模型中COM-Poisson分布的研究,以改进预测
Suryo Adi Rakhmawan1, Tahir Mahmood2, Nasir Abbas3
1Department of Social Statistics, BPS-Statistics Indonesia, Jakarta, 10710, Indonesia.
Lifetime data analysis
|September 13, 2024
概括
使用COM-Poisson通用自回归分数 (GAS) 模型,预测死亡率得到了改进. 这种先进的死亡预测方法为保险偿付能力评估提供了更高的准确性和灵活性.
科学领域:
- 精算科学 精算科学
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 准确的死亡率预测对于人寿保险的偿付能力至关重要,尤其是在COVID-19等全球性干扰的情况下.
- 传统的李-卡特模型在处理各种数据分布方面存在局限性.
- 需要先进的统计模型来提高时间到事件预测的准确性.
研究的目的:
- 评估COM-Poisson分布在通用自回归得分 (GAS) 模型中的有效性,用于死亡率预测.
- 为了比较COM-Poisson GAS模型与传统分布 (Poisson,二项式,负二项式) 的预测性能.
- 评估模型适用于时间序列死亡率数据的适用性,具有时间变化的参数和非传统分布.
主要方法:
- 利用来自29个国家的死亡率数据.
- 实施和评估了使用COM-Poisson分布的通用自回归得分 (GAS) 模型.
- 与Poisson,二项式和负二项式分布对比预测准确度.
主要成果:
- 在预测死亡率方面,COM-Poisson模型与Poisson,二项式和负二项式分布相比,表现优越.
- GAS模型的一步预测能力被证明是有利的.
- COM-Poisson分布在适应各种数据分布方面表现出灵活性,包括Poisson和负二项式.
结论:
- COM-Poisson GAS模型是分析时间序列死亡率数据的有效工具.
- 这种模型在处理时间变化的参数和非传统数据分布时特别有用.
- 这些发现支持提高生命保险和精算应用的死亡率预测的准确性.
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