合成数据在费率制定中的应用：基于填补的方法与对抗网络和自编码器
Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders
AI总结：
本文比较了基于填补的方法与对抗网络和自编码器在合成数据生成中的表现，发现基于MICE的方法在生成高质量表格数据方面具有较低的实现复杂性。
摘要：
精算费率制定依赖于高质量的数据，但获取此类数据常常受到获取新数据成本、隐私问题等因素的限制。本文探讨了合成数据生成作为解决这些问题的潜在方法。除了在精算文献中已研究过的生成方法外，我们还探索并评估了基于多变量链式填补（MICE）的另一类方法。在使用开源数据集的比较研究中，基于MICE的模型被评估与其他生成模型如变分自编码器和条件表格生成对抗网络。我们评估了合成数据在保持原始变量边缘分布以及协变量之间的多变量关系方面的效果。还研究了在合成数据上训练的广义线性模型（GLMs）与在原始数据上训练的GLMs之间的一致性。此外，我们还评估了每种生成方法的易用性，并研究了通用地将原始数据与合成数据结合对GLMs预测赔款数量性能的影响。我们的结果突显了基于MICE的方法在创建高保真表格数据方面的潜力，同时相比深度生成模型具有较低的实现复杂性。
英文摘要：
Actuarial ratemaking depends on high-quality data, yet access to such data is often limited by the cost of obtaining new data, privacy concerns, etc. In this paper, we explore synthetic-data generation as a potential solution to these issues. In addition to generative methods previously studied in the actuarial literature, we explore and benchmark another class of approaches based on Multivariate Imputation by Chained Equations (MICE). In a comparative study using an open-source dataset, MICE-based models are evaluated against other generative models like Variational Autoencoders and Conditional Tabular Generative Adversarial Networks. We assess how well synthetic data preserves the original marginal distributions of variables as well as the multivariate relationships among covariates. The consistency between Generalized Linear Models (GLMs) trained on synthetic data with GLMs trained on the original data is also investigated. Furthermore, we assess the ease of use of each generative approach and study the impact of generically augmenting original data with synthetic data on the estimation of GLMs for predicting claim counts. Our results highlight the potential of MICE-based methods in creating high-fidelity tabular data while offering lower implementation complexity compared to deep generative models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
