Analysing Cause-Specific Mortality Trends using Compositional Functional Data Analysis
研究将各国特定原因死亡率视为函数成分，开发新框架进行主成分分析和聚类，揭示男女死亡率趋势差异，如肺癌发病率男性稳定而女性上升。
Abstract We study the dynamics of cause-specific mortality rates among countries by considering them as compositions of functions. We develop a novel framework for such data structure, with particular attention to functional PCA. The application of this method to a subset of the WHO mortality database reveals the main modes of variation of cause-specific rates over years for men and women and enables us to perform clustering in the projected subspace. The results give many insights of the ongoing trends, only partially explained by past literature, that the considered countries are undergoing. We are also able to show the different evolution of cause of death undergone by men and women: for example, we can see that while lung cancer incidence is stabilizing for men, it is still increasing for women.