In this paper, we suggest a novel method for detecting mortality deceleration. We focus on the gamma-Gompertz frailty model and suggest the subtraction of a penalty in the log-likelihood function as an alternative to traditional likelihood inference and hypothesis testing. Over existing methods, our method offers advantages, such as avoiding the use of a p-value, hypothesis testing, and asymptotic distributions. We evaluate the performance of our approach by comparing it with traditional likelihood inference on both simulated and real mortality data. Results have shown that our approach is more accurate in detecting mortality deceleration and provides more reliable estimates of the underlying parameters. The proposed method is a significant contribution to the literature as it offers a powerful tool for analyzing mortality patterns.
翻译:在本文中,我们建议采用一种新的方法来检测死亡率减速。我们侧重于伽马-贡佩茨脆弱模型,并建议在日志类功能中减法,以替代传统的概率推断和假设测试。在现有的方法中,我们的方法提供了优势,例如避免使用P值、假设测试和无药可救分布。我们通过将我们的方法与模拟和实际死亡率数据的传统可能性推法进行比较来评估我们的方法的绩效。结果显示,我们的方法在检测死亡率减速方面更为准确,并且提供了对基本参数的更可靠的估计。提议的方法是对文献的一大贡献,因为它为分析死亡率模式提供了强有力的工具。