{"title":"通过高斯过程核建立具有表现力的死亡率模型","authors":"Jimmy Risk, Mike Ludkovski","doi":"10.1017/asb.2023.39","DOIUrl":null,"url":null,"abstract":"We develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance prior best matching the spatio-temporal dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA) on synthetic case studies to validate the ability of the GA to recover APC structure and on real-life national-level datasets from the Human Mortality Database. Our machine learning-based analysis provides novel insight into the presence/absence of Cohort effects in different populations and into the relative smoothness of mortality surfaces along the Age and Year dimensions. Our modeling work is done with the PyTorch libraries in Python and provides an in-depth investigation of employing GA to aid in compositional kernel search for GP surrogates.","PeriodicalId":501189,"journal":{"name":"ASTIN Bulletin: The Journal of the IAA","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Expressive mortality models through Gaussian process kernels\",\"authors\":\"Jimmy Risk, Mike Ludkovski\",\"doi\":\"10.1017/asb.2023.39\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance prior best matching the spatio-temporal dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA) on synthetic case studies to validate the ability of the GA to recover APC structure and on real-life national-level datasets from the Human Mortality Database. Our machine learning-based analysis provides novel insight into the presence/absence of Cohort effects in different populations and into the relative smoothness of mortality surfaces along the Age and Year dimensions. Our modeling work is done with the PyTorch libraries in Python and provides an in-depth investigation of employing GA to aid in compositional kernel search for GP surrogates.\",\"PeriodicalId\":501189,\"journal\":{\"name\":\"ASTIN Bulletin: The Journal of the IAA\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ASTIN Bulletin: The Journal of the IAA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/asb.2023.39\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ASTIN Bulletin: The Journal of the IAA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/asb.2023.39","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
我们开发了一个灵活的高斯过程(GP)框架,用于学习特定年龄和年份死亡率表面的协方差结构。利用 GP 内核的加法和乘法结构,我们设计了一种遗传编程算法,为给定人群搜索最具表现力的内核。我们的组成搜索建立在年龄-时期-队列(APC)范式的基础上,以构建最符合死亡率数据集时空动态的协方差先验。我们将由此产生的遗传算法(GA)应用于合成案例研究,以验证遗传算法恢复 APC 结构的能力,并应用于人类死亡率数据库中的真实国家级数据集。我们基于机器学习的分析对不同人群中是否存在队列效应以及死亡率表面在年龄和年份维度上的相对平滑性提供了新的见解。我们的建模工作是通过 Python 中的 PyTorch 库完成的,并深入研究了如何利用 GA 来帮助 GP 代理的组成核搜索。
Expressive mortality models through Gaussian process kernels
We develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance prior best matching the spatio-temporal dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA) on synthetic case studies to validate the ability of the GA to recover APC structure and on real-life national-level datasets from the Human Mortality Database. Our machine learning-based analysis provides novel insight into the presence/absence of Cohort effects in different populations and into the relative smoothness of mortality surfaces along the Age and Year dimensions. Our modeling work is done with the PyTorch libraries in Python and provides an in-depth investigation of employing GA to aid in compositional kernel search for GP surrogates.