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Bayesian modeling of the evolution of male height in 18th century Finland from incomplete data

机译:贝叶斯模型从不完整数据中对18世纪芬兰男性身高演变的建模

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摘要

Data on army recruits' height are frequently available and can be used to analyze the economics and welfare of the population in different periods of history. However, such data are not a random sample from the whole population at the time of interest, but instead is skewed since the short men were less likely to be recruited. In statistical terms this means that the data are left-truncated. Although truncation is well-understood in statistics a further complication is that the truncation threshold is not known, may vary from time to time, and auxiliary information on the threshold is not at our disposal. The advantage of the fully Bayesian approach presented here is that both the population height distribution and the truncation are modeled simultaneously. The truncation threshold is allowed to be random and time-specific whilst the height distribution is assumed to change smoothly in time. Thus, in addition to the population height characteristics, we obtain also insight into recruiting criteria over time. Analysis of historical data from Swedish army recruitment in eight time events between 1768 and 1804 has found a declining trend in the mean population height during the inspected time period and also dramatic systematic changes in the recruiting.
机译:有关新兵身高的数据经常可用,可用于分析不同历史时期人口的经济和福利。但是,这些数据并不是在感兴趣时从整个人群中随机抽取的数据,而是偏斜的,因为矮个子的人被招募的可能性较小。用统计术语来说,这意味着数据将被截断。尽管截断在统计数据中已广为人知,但更复杂的是截断阈值未知,可能会不时变化,并且无法使用阈值的辅助信息。这里介绍的完全贝叶斯方法的优点在于,可以同时对种群高度分布和截断进行建模。截断阈值可以是随机的并且是特定于时间的,而高度分布被假定为随时间平滑变化。因此,除了人口高度特征外,我们还获得了随着时间推移对招聘标准的了解。对瑞典军队在1768年至1804年期间发生的八次征兵的历史数据进行的分析发现,在检查的时间段内,平均人口高度呈下降趋势,并且征兵也发生了系统的急剧变化。

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