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Alleviating linear ecological bias and optimal design with subsample data

机译:利用子样本数据缓解线性生态偏差和优化设计

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摘要

We illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate provides two main benefits. First, by including the individual level subsample data, the biases that are associated with linear ecological inference can be eliminated. Second, available ecological data can be used to design optimal subsampling schemes that maximize information about parameters. We present an application of this methodology to the classic problem of estimating the effect of a college degree on wages, showing that small, optimally chosen subsamples can be combined with ecological data to generate precise estimates relative to a simple random subsample.
机译:我们说明,在线性模型合适的情况下,将生态数据与子样本数据结合起来可提供两个主要好处。首先,通过包含各个级别的子样本数据,可以消除与线性生态推断相关的偏差。其次,可用的生态数据可用于设计最佳的二次采样方案,以最大化有关参数的信息。我们将这种方法应用于估算大学学位对工资影响的经典问题,结果表明,可以将最佳选择的小样本与生态数据相结合,以生成相对于简单随机子样本的精确估计。

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