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Robust variance estimators for generalized regression estimators in cluster samples

机译:集群样本中的广义回归估计的强大方差估计

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摘要

Standard linearization estimators of the variance of the general regression estimator are often too small, leading to confidence intervals that do not cover at the desired rate. Hat matrix adjustments can be used in two-stage sampling that help remedy this problem. We present theory for several new variance estimators and compare them to standard estimators in a series of simulations. The proposed estimators correct negative biases and improve confidence interval coverage rates in a variety of situations that mirror ones that are met in practice.
机译:一般回归估计器的方差的标准线性化估计通常太小,导致不以所需速率覆盖的置信区间。帽子矩阵调整可用于两级采样,有助于解决此问题。我们对几个新差异估算器提供理论,并将它们与标准估计进行了一系列模拟。所提出的估计人可以在镜像在实践中遇到的各种情况下,提高置信区间覆盖率。

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