Pedroza Claudia
Division of Biostatistics, University of Texas School of Public Health at Houston, Houston, TX 77030, USA.
Biostatistics. 2006 Oct;7(4):530-50. doi: 10.1093/biostatistics/kxj024. Epub 2006 Feb 16.
This article presents a Bayesian approach to forecast mortality rates. This approach formalizes the Lee-Carter method as a statistical model accounting for all sources of variability. Markov chain Monte Carlo methods are used to fit the model and to sample from the posterior predictive distribution. This paper also shows how multiple imputations can be readily incorporated into the model to handle missing data and presents some possible extensions to the model. The methodology is applied to U.S. male mortality data. Mortality rate forecasts are formed for the period 1990-1999 based on data from 1959-1989. These forecasts are compared to the actual observed values. Results from the forecasts show the Bayesian prediction intervals to be appropriately wider than those obtained from the Lee-Carter method, correctly incorporating all known sources of variability. An extension to the model is also presented and the resulting forecast variability appears better suited to the observed data.
本文提出了一种预测死亡率的贝叶斯方法。该方法将李-卡特方法形式化为一个统计模型，该模型考虑了所有变异性来源。马尔可夫链蒙特卡罗方法用于拟合模型并从后验预测分布中抽样。本文还展示了如何轻松地将多重插补纳入模型以处理缺失数据，并提出了对该模型的一些可能扩展。该方法应用于美国男性死亡率数据。基于1959年至1989年的数据，形成了1990年至1999年期间的死亡率预测。将这些预测与实际观测值进行比较。预测结果表明，贝叶斯预测区间比从李-卡特方法获得的区间更宽，正确地纳入了所有已知的变异性来源。还提出了对该模型的扩展，由此产生的预测变异性似乎更适合观测数据。
