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arXiv 1606.05067stat.AP
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发达国家一组人口的死亡率和预期寿命预测:一种多水平函数数据方法

Mortality and life expectancy forecasting for a group of populations in developed countries: A multilevel functional data method

Han Lin Shang

摘要:

在拥有高质量人口动态登记系统的发达国家,采用多级函数数据方法预测两个或两个以上人口的年龄别死亡率。它使用多层次的功能主成分分析的总体和特定群体的数据,提取的共同趋势和特定群体的残差趋势之间的群体。如果特定人群的残差趋势的预测没有显示出长期趋势,则可能实现预测的收敛。该方法首先适用于英国的年龄和性别特定的数据,其预测精度,然后进一步比较与现有的几种方法,包括独立的功能数据和产品比率的方法,通过多国比较。所提出的方法也证明了年龄,性别和国家的具体数据在澳大利亚,在预测的收敛可能会实现性别和国家。对于年龄别死亡率的预测,多水平函数数据方法比其他相关方法更准确。在预测女性出生时预期寿命时,多水平函数数据方法优于\cite{RLG14}的贝叶斯方法。对于男性出生时预期寿命的预测,多水平函数数据方法在点预测方面优于贝叶斯方法,但在区间预测方面则不如贝叶斯方法。本文的补充材料可在网上查阅。

英文摘要:

A multilevel functional data method is adapted for forecasting age-specific mortality for two or more populations in developed countries with high-quality vital registration systems. It uses multilevel functional principal component analysis of aggregate and population-specific data to extract the common trend and population-specific residual trend among populations. If the forecasts of population-specific residual trends do not show a long-term trend, then convergence in forecasts may be achieved. This method is first applied to age- and sex-specific data for the United Kingdom, and its forecast accuracy is then further compared with several existing methods, including independent functional data and product-ratio methods, through a multi-country comparison. The proposed method is also demonstrated by age-, sex- and state-specific data in Australia, where the convergence in forecasts can possibly be achieved by sex and state. For forecasting age-specific mortality, the multilevel functional data method is more accurate than the other coherent methods considered. For forecasting female life expectancy at birth, the multilevel functional data method is outperformed by the Bayesian method of \cite{RLG14}. For forecasting male life expectancy at birth, the multilevel functional data method performs better than the Bayesian methods in terms of point forecasts, but less well in terms of interval forecasts. Supplementary materials for this article are available online.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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