Using a Penalized Likelihood to Detect Mortality Deceleration

In this paper, we propose a novel approach to detect deceleration in mortality patterns. We focus on a gamma-Gompertz frailty model, and suggest maximizing a penalized likelihood in a Bayesian setting as an alternative to traditional likelihood inference and hypothesis testing. Our method offers several advantages over existing methods, such as a more robust and flexible way of modeling mortality patterns. We evaluate the performance of our method by comparing it with traditional likelihood inference on both simulated and real mortality data. The results show that our approach is more accurate in detecting deceleration in mortality patterns, and also provides more reliable estimates of the underlying parameters. The proposed method is an important addition to the existing literature as it offers a powerful tool for analyzing mortality patterns in populations.