Measuring Inequality Using Censored Data: A Multiple Imputation Approach
Measuring Inequality Using Censored Data: A Multiple Imputation Approach
复制标题
使用审查数据衡量不平等：多重插补方法
DOI：
10.2139/ssrn.1431352
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Jeff Larrimore
中科院分区：
文献类型：
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作者：
S. Jenkins;R. Burkhauser;S. Feng;Jeff Larrimore
为了用右删节（顶部编码）数据衡量收入不平等，我们提出了用于估计和推断的多重imputation方法。截尾观测是用一个适应截尾分布的灵活参数模型的图进行多重输入的，产生一个部分合成的数据集，从中可以使用完整数据方法和适当的组合公式推导出点和方差估计。这些方法是用美国当前人口调查数据和第二类分布的广义贝塔作为输入模型来说明的。根据当前人口调查的内部数据，我们发现1995年至2004年的收入不平等在统计上几乎没有显著差异。我们还表明，使用当前人口调查公共使用数据与细胞平均impuimpu法可能导致不正确的推断。多重输入的公共使用数据提供了一种中间解决方案。
To measure income inequality with right-censored (top-coded) data, we propose multiple-imputation methods for estimation and inference. Censored observations are multiply imputed using draws from a flexible parametric model fitted to the censored distribution, yielding a partially synthetic data set from which point and variance estimates can be derived using complete-data methods and appropriate combination formulae. The methods are illustrated using US Current Population Survey data and the generalized beta of the second kind distribution as the imputation model. With Current Population Survey internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using Current Population Survey public use data with cell mean imputations may lead to incorrect inferences. Multiply-imputed public use data provide an intermediate solution.