不确定性估计下寿命预测的Lee-Carter深化
Deepening Lee-Carter for longevity projections with uncertainty estimation
摘要：
毫无疑问，世界上有几个国家的预期寿命持续增长，这给寿命精算师和人口统计学家预测死亡率带来了挑战。虽然在过去的文献中已经提出了一些随机死亡率模型，死亡率预测的研究仍然是一个至关重要的任务。最近，各种研究工作鼓励深度学习模型在死亡率数据中推断合适的模式。这种学习模型允许实现准确的点预测，尽管也需要不确定性措施来支持模型估计的可靠性和风险评估。据我们所知，死亡率预测中的机器和深度学习文献缺乏关于不确定性估计的研究。作为死亡率预测的新进展，我们在Lee-Carter框架内形式化了深度神经网络集成，在深度学习和死亡率密度预测之间架起了第一座桥梁。我们在一个数值应用程序中测试了我们的模型建议，考虑了全球三个有代表性的国家和两种性别，仔细检查了两个不同的拟合期。利用预测的生物合理性和可行性以及性能指标的意义，我们的研究结果证实了深度学习模型对提高Lee-Carter模型预测能力的适用性，从长远来看也提供了更可靠的死亡率边界。
英文摘要：
Undoubtedly, several countries worldwide endure to experience a continuous increase in life expectancy, extending the challenges of life actuaries and demographers in forecasting mortality. Although several stochastic mortality models have been proposed in past literature, the mortality forecasting research remains a crucial task. Recently, various research works encourage the adequacy of deep learning models to extrapolate suitable pattern within mortality data. Such a learning models allow to achieve accurate point predictions, albeit also uncertainty measures are necessary to support both model estimates reliability and risk evaluations. To the best of our knowledge, machine and deep learning literature in mortality forecasting lack for studies about uncertainty estimation. As new advance in mortality forecasting, we formalizes the deep Neural Networks integration within the Lee-Carter framework, posing a first bridge between the deep learning and the mortality density forecasts. We test our model proposal in a numerical application considering three representative countries worldwide and both genders, scrutinizing two different fitting periods. Exploiting the meaning of both biological reasonableness and plausibility of forecasts, as well as performance metrics, our findings confirm the suitability of deep learning models to improve the predictive capacity of the Lee-Carter model, providing more reliable mortality boundaries also on the long-run.
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。
