A Unified Monte-Carlo Jackknife for Small Area Estimation after Model Selection
A Unified Monte-Carlo Jackknife for Small Area Estimation after Model Selection
复制标题
模型选择后小面积估计的统一蒙特卡洛折刀法
DOI：
10.4310/amsa.2018.v3.n2.a2
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Thuan Nguyen
中科院分区：
文献类型：
--
作者：
Jiming Jiang;P. Lahiri;Thuan Nguyen
在小区域估计(SAE)中，当在估计之前涉及一个模型选择过程时，我们考虑不确定度的估计。本文提出了一种估计均方预测误差对数的统一蒙特卡罗刀法，称为McJack。我们证明了McJack的二阶无偏性，并通过包括模拟研究和真实数据分析在内的实证研究，论证了McJack在模型选择后对SAE中的不确定性进行评估的性能。
We consider estimation of measure of uncertainty in small area estimation (SAE) when a procedure of model selection is involved prior to the estimation. A unified Monte-Carlo jackknife method, called McJack, is proposed for estimating the logarithm of the mean squared prediction error. We prove the second-order unbiasedness of McJack, and demonstrate the performance of McJack in assessing uncertainty in SAE after model selection through empirical investigations that include simulation studies and real-data analyses.