使用惩罚似然检测死亡率减速
Using a Penalized Likelihood to Detect Mortality Deceleration
AI总结：
本文提出一种基于惩罚似然的死亡率减速检测方法，替代传统似然推断，避免p值与假设检验，在模拟和真实数据上更准确且参数估计更可靠。
摘要：
本文提出了一种检测死亡率减速的新方法。我们聚焦于gamma-Gompertz脆弱性模型，并建议在对数似然函数中减去一个惩罚项，作为传统似然推断和假设检验的替代方案。与现有方法相比，我们的方法具有优势，例如避免了p值、假设检验和渐近分布的使用。我们通过将我们的方法与传统的似然推断在模拟和真实死亡率数据上进行比较来评估其性能。结果表明，我们的方法在检测死亡率减速方面更为准确，并提供了对潜在参数的更可靠估计。所提出的方法是对文献的重要贡献，因为它为分析死亡率模式提供了一个强大的工具。
英文摘要：
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.
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。
