Forecasting causes of death by using compositional data analysis: the case of cancer deaths

作     者：Kjaergaard, Soren Ergemen, Yunus Emre Kallestrup-Lamb, Malene Oeppen, Jim Lindahl-Jacobsen, Rune 

作者机构：Univ Southern Denmark Odense Denmark Aarhus Univ Aarhus Denmark Copenhagen Business Sch Copenhagen Denmark 

出 版 物：《JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS》 (皇家统计学会志，C辑：应用统计学)

年 卷 期：2019年第68卷第5期

页      面：1351-1370页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Danish pension funds and Copenhagen Business School AXA Research Fund, AXA 

主　　题：Cancer forecast Cause-specific mortality Compositional data analysis Forecasting methods Population health 

摘      要：Cause-specific mortality forecasting is often based on predicting cause-specific death rates independently. Only a few methods have been suggested that incorporate dependence between causes. An attractive alternative is to model and forecast cause-specific death distributions, rather than mortality rates, as dependence between the causes can be incorporated directly. We follow this idea and propose two new models which extend the current research on mortality forecasting using death distributions. We find that adding age, time and cause-specific weights and decomposing both joint and individual variation between different causes of death increased the forecast accuracy of cancer deaths by using data for French and Dutch populations.