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Bayesian small area demography

机译：贝叶斯小区域人口统计学

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摘要

Demographers are facing increasing pressure to disaggregate their estimates and forecasts by characteristics such as region, ethnicity, and income. Traditional demographic methods were designed for large samples, and perform poorly with disaggregated data. Methods based on formal Bayesian statistical models offer better performance. We illustrate with examples from a long-term project to develop Bayesian approaches to demographic estimation and forecasting. In our first example, we estimate mortality rates disaggregated by age and sex for a small population. In our second example, we simultaneously estimate and forecast obesity prevalence disaggregated by age. We conclude by addressing two traditional objections to the use of Bayesian methods in statistical agencies.
机译：人口统计学家面临越来越大的压力，需要按地区，种族和收入等特征来分类估计和预测。传统的人口统计方法是为大型样本设计的，对分类数据的处理效果较差。基于正式贝叶斯统计模型的方法可提供更好的性能。我们用一个长期项目的例子来说明，以开发贝叶斯方法进行人口估计和预测。在我们的第一个例子中，我们估计了按人口年龄和性别分类的死亡率。在第二个示例中，我们同时估算和预测了按年龄分类的肥胖症患病率。我们以解决在统计机构中使用贝叶斯方法的两个传统反对意见作为结论。

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