Small Area Forecasts of Cause-Specific Mortality: Application of a Bayesian Hierarchical Model to US Vital Registration Data
提出一种贝叶斯分层模型，联合预测美国各州多种死因的死亡率，利用1979-2011年数据预测至2024年，误差低于常用模型。
Summary Mortality forecasts are typically limited in that they pertain only to national death rates, predict only all-cause mortality or do not capture and utilize the correlation between diseases. We present a novel Bayesian hierarchical model that jointly forecasts cause-specific death rates for geographic subunits. We examine its effectiveness by applying it to US vital statistics data for 1979–2011 and produce forecasts to 2024. Not only does the model generate coherent forecasts for mutually exclusive causes of death, but also it has lower out-of-sample error than alternative commonly used models for forecasting mortality.