Testing additivity in two-way classifications with no replications:the locally best invariant test
Testing additivity in two-way classifications with no replications:the locally best invariant test
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在没有重复的双向分类中测试可加性：局部最佳不变测试
DOI：
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发表时间：
1993
期刊：
影响因子：
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通讯作者：
R. J. Boik
中科院分区：
文献类型：
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作者：
R. J. Boik
在每个单元具有一个观测的双向分类中，通常认为行x列相互作用可以忽略不计。没有交互作用，研究人员可以估计实验误差，并继续对行和列效应进行推断。如果可加性受到怀疑，常规做法是对照结构化替代方案进行测试。如果结构化选择没有指定现有的非可加性，那么即使现有的非可加性的大小很大，检验的功率也很低。可加性的局部最佳不变量(LBI)检验较少受到模型误指定的影响，因为不需要假设特定的结构备选方案。本文阐述了可加性的LBI检验，并将其威力与Johnson-GrayBill似然比(LR)检验进行了比较。在Johnson-GrayBill替代方案下，LBI测试的表现与LR测试一样好，在更一般的替代方案下，LBI测试的表现优于LR测试。
Row x column interaction is frequently assumed to be negligible in two-way classifications having one observation per cell. Absence of interaction allows the researcher to estimate experimental error and to proceed with making inferences about row and column effects. If additivity is suspect, it is conventional to test it against a structured alternative. If the structured alternative missspecifies the existing nonadditivity, then the power of the test is low, even if the magnitude of the existing nonadditivity is large. The locally best invariant (LBI) test of additivity is less subject to model misspecification because a particular structural alternative need not be hypothesized. This paper illustrates the LBI test of additivity and compares its power to that of the Johnson-Graybill likelihood ratio (LR) test. The LBI test performs as well as the LR test under a Johnson-Graybill alternative and performs better than the LR test under more general alternatives.