In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the...In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.展开更多
基于Neyman-Rubin潜在结果框架,构建k近邻核估计量来测度响应变量随机缺失情形下的条件平均处理效应(conditional average treatment effect,CATE),旨在评估不同处理方式对个体的影响.证明了k近邻核估计量的几乎完全收敛性和渐近正态性...基于Neyman-Rubin潜在结果框架,构建k近邻核估计量来测度响应变量随机缺失情形下的条件平均处理效应(conditional average treatment effect,CATE),旨在评估不同处理方式对个体的影响.证明了k近邻核估计量的几乎完全收敛性和渐近正态性.数值模拟表明k近邻核估计量的表现优良,利用真实数据进行实证分析,实证结果显示k近邻核估计量具有较小的平均绝对偏差和均方根误差.展开更多
基金supported by the National Science Foundation of China under Grant Nos.71361015,71340010,71371074the Jiangxi Provincial Natural Science Foundation under Grant No.20142BAB201013+2 种基金China Postdoctoral Science Foundation under Grant No.2013M540534China Postdoctoral Fund special Project under Grant No.2014T70615Jiangxi Postdoctoral Science Foundation under Grant No.2013KY53
文摘In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.
文摘基于Neyman-Rubin潜在结果框架,构建k近邻核估计量来测度响应变量随机缺失情形下的条件平均处理效应(conditional average treatment effect,CATE),旨在评估不同处理方式对个体的影响.证明了k近邻核估计量的几乎完全收敛性和渐近正态性.数值模拟表明k近邻核估计量的表现优良,利用真实数据进行实证分析,实证结果显示k近邻核估计量具有较小的平均绝对偏差和均方根误差.
