The score test for independence in R x C contingency tables with missing data.
The score test for independence in R x C contingency tables with missing data.
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DOI：
10.2307/2532915
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发表时间：
1996-06
期刊：
影响因子：
1.9
通讯作者：
S. Lipsitz;G. Fitzmaurice
中科院分区：
文献类型：
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作者：
S. Lipsitz;G. Fitzmaurice
本文提出了缺失数据情形下R × C列联表独立性检验的Score检验统计量。在独立性的零假设下，统计量具有近似的卡方分布，自由度为（R - 1）（C - 1）。该检验统计量与具有完整数据的Pearson卡方统计量非常相似，并且与检验独立性的似然比统计量不同，其计算简单且非迭代。此外，当R × C表的行和列是有序的时，提出了一个分数检验统计量来检验独立性。最后，扩展的分数统计测试条件的独立性在一组（R × C）列联表与缺失数据进行了说明。这产生了分数检验统计量，它们是Mantel-Haenszel统计量的自然扩展。一个例子，使用一个子集的数据从六个城市的研究，来说明的方法。
In this paper, the score test statistic for testing independence in R x C contingency tables with missing data is proposed. Under the null hypothesis of independence, the statistic has an approximate chi-squared distribution with (R - 1)(C - 1) degrees of freedom. The proposed test statistic is quite similar to the Pearson chi-squared statistic with complete data and, unlike the likelihood ratio statistic for testing independence, its computation is simple and noniterative. In addition, a score test statistic is proposed for testing independence when the rows and columns of the R x C table are ordinal. Finally, extensions of the score statistics to test for conditional independence in a set of (R x C) contingency tables with missing data are described. This yields score test statistics that are natural extensions of the Mantel-Haenszel statistic. An example, using a subset of data from the Six Cities Study, is presented to illustrate the methods.