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Calibration of estimator-weights via semismooth Newton method

机译:通过半光滑牛顿法校正估计量

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摘要

Weighting is a common methodology in survey statistics to increase accuracy of estimates or to compensate for non-response. One standard approach for weighting is calibration estimation which represents a common numerical problem. There are various approaches in the literature available, but quite a number of distance-based approaches lack a mathematical justification or are numerically unstable. In this paper we reformulate the calibration problem as a system of nonlinear equations. Although the equations are lacking differentiability properties, one can show that they are semismooth and the corresponding extension of Newton's method is applicable. This is a mathematically rigorous approach and the numerical results show the applicability of this method.
机译:加权是调查统计中的一种常用方法,可以提高估算的准确性或补偿无答复。加权的一种标准方法是校准估计,它代表一个常见的数值问题。现有文献中有各种方法,但是很多基于距离的方法缺乏数学上的依据或数值上不稳定。在本文中,我们将校准问题重新构造为非线性方程组。尽管这些方程缺乏微分性质,但可以证明它们是半光滑的,牛顿法的相应扩展是适用的。这是一种严格的数学方法,数值结果证明了该方法的适用性。

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