Bounds on Generalized Linear Predictors with incomplete outcome data
Bounds on Generalized Linear Predictors with incomplete outcome data
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DOI：
10.1007/s11155-006-9030-5
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发表时间：
2007-06-01
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通讯作者：
Stoye, Joerg
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文献类型：
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作者：
Stoye, Joerg
本文发展了当结果数据被部分识别时，广义线性预报器和工具变量估计量的易于计算的紧界。一个突出的例子是平方损失下的最佳线性预测，或普通最小二乘回归，在这种情况下，设置专门研究更一般但棘手的问题，由Horowitz等人研究。[9]。通过对论文中所用数据的重新分析，说明了这一结果。
This paper develops easily computed, tight bounds on Generalized Linear Predictors and instrumental variable estimators when outcome data are partially identified. A salient example is given by Best Linear Predictors under square loss, or Ordinary Least Squares regressions, with missing outcome data, in which case the setup specializes the more general but intractable problem examined by Horowitz et al. [9]. The result is illustrated by re-analyzing the data used in that paper.