一类地理加权CBD死亡率预测模型的研究
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摘要
死亡率预测一直是人口学中的重要问题,目前得到研究较多的死亡率预测模型研究的大都是基于单一地区的多年份年龄别死亡率数据.考虑多个地区的单一年份的分年龄死亡率数据,基于死亡率在高年龄段随年龄的增长不断提高以及地理位置越靠近的地区这种变化趋势越类似两个特征,提出了一类地理加权CBD模型,并利用该方法分析了我国2010年第六次人口普查省域死亡率数据.
The prediction of mortality rates is one kind of important research in demographic. Former researches most commonly focused on studying one area using time series data of several years' mortality rates. This article concerns about studying different areas' mortality rates but in one specific year. Noticing that among those elderly people, the older they are the higher their mortality rates will be and this feature of mortality rate is vary from area to area. But the more the areas are near to each other the more their features are similar. Based on this finding, this article has build a Spatially-CBD model and used the new model to study the 31 Chinese provinces' mortality rates in 2010 year.
The prediction of mortality rates is one kind of important research in demographic. Former researches most commonly focused on studying one area using time series data of several years' mortality rates. This article concerns about studying different areas' mortality rates but in one specific year. Noticing that among those elderly people, the older they are the higher their mortality rates will be and this feature of mortality rate is vary from area to area. But the more the areas are near to each other the more their features are similar. Based on this finding, this article has build a Spatially-CBD model and used the new model to study the 31 Chinese provinces' mortality rates in 2010 year.
引文
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[4] Lee R D, Carter L R. Modelling and forecasting the time series of U.S. mortality[J]. Journal of the American Statistical Association, 1992, 87(419):659-671.
[5] Li H, O'Hare C, Zhang X. A Semiparametric Panel Approach to Mortality Modeling[J]. Insurance:Mathematics and Economics, 2015, 61, 264-270.
[6] Haberman S, Renshaw A E. A Comparative Study of Parametric Mortality Projection Models[J].Insurance:Mathematics and Economics, 2011, 48(1):35-55.
[2] Cairns A J G, Blake D, Dowd K. A two-factor model for stochastic mortality with parameter uncertainty:theory and calibration[J]. Journal of Risk and Insurance, 2006, 73(4):687-718.
[3] Fotheringham A S, Charlton M E, Brunsdon C. Geographically Weighted Regression:The Analysis of Spatially Varying Relationships[M]. New York:Wiley, 2002.
[4] Lee R D, Carter L R. Modelling and forecasting the time series of U.S. mortality[J]. Journal of the American Statistical Association, 1992, 87(419):659-671.
[5] Li H, O'Hare C, Zhang X. A Semiparametric Panel Approach to Mortality Modeling[J]. Insurance:Mathematics and Economics, 2015, 61, 264-270.
[6] Haberman S, Renshaw A E. A Comparative Study of Parametric Mortality Projection Models[J].Insurance:Mathematics and Economics, 2011, 48(1):35-55.