On distance based goodness of fit tests for missing data when missing occurs at random

Pub Date : 2021-05-30 DOI:10.1111/anzs.12313
Subhra Sankar Dhar, Ujjwal Das
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Abstract

Various non-parametric goodness of fit tests have already been investigated in the literature. However, those tests are rarely used in the case of missing observations. We here study the goodness of fit test for missing data based on Lp distances along with Kolmogorov–Smirnov and Cramer–von-Mises distances when missingness occurs at random. The asymptotic distributions of the proposed test statistics have been derived under contiguous alternatives that enable us to investigate the asymptotic local power of the tests. We also study the performance of the tests for finite samples using simulation, and the tests perform well for those cases. The usefulness of the tests is illustrated on three real data sets.

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缺失数据随机发生时基于距离的拟合优度检验
各种非参数拟合优度检验已经在文献中进行了研究。但是,在缺少观测值的情况下很少使用这些测试。本文研究了随机缺失发生时,基于Lp距离以及Kolmogorov-Smirnov和crmer - von- mises距离对缺失数据的拟合优度检验。所提出的检验统计量的渐近分布已经在相邻的备选项下得到,使我们能够研究检验的渐近局部幂。本文还对有限样本下的测试性能进行了仿真研究,结果表明,在这种情况下,测试效果良好。在三个实际数据集上说明了测试的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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