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Hypothesis testing of equality between exponential distributions with matched sets

机译:具有匹配集的指数分布之间相等性的假设检验

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In matched design, if the unit cost from one comparison group is higher than the unit cost from the other group, then one can consider matching each unit randomly selected from the former with more than one unit from the latter, to increase power of the test. This paper extends the discussion on testing equality between exponential distributions for one-to-one paired design to that for K-to-one matched design, where K can be any finite positive integer. This paper considers the asymptotic test procedure using the central limit and Fieller's theorems (CLFT), the asymptotic test procedure using the marginal likelihood ratio test (MLRT), an exact parametric test (EXPT) and applies Monte Carlo simulation to evaluate the performance of these procedures. When the number of matched sets, n, is as small as 10, the estimated type-I error for the two asymptotic procedures can still agree well with the nominal level. When the number of matched units, K, exceeds 4, the effect due to an increase in K on power generally becomes minimal. When the intra-class correlation between failure times within matched sets is small, using the CLFT generally has larger power than using either the MLRT or EXPT in one-to-one paired design. On the other hand, when the intra-class correlation between failure times within matched sets is large, the power for the MLRT is higher than the power for both the CLFT and EXPT in almost all the situations considered in this paper. Hence the author recommends the MLRT.
机译:在匹配设计中,如果一个比较组的单位成本高于另一组的单位成本,则可以考虑将前者随机选择的每个单位与后者的一个以上单位进行匹配,以提高测试的功效。本文将关于测试一对一配对设计的指数分布之间的相等性的讨论扩展到了针对一对一匹配设计的指数分布之间的相等性的讨论,其中K可以是任何有限的正整数。本文考虑了使用中心极限和Fieller定理(CLFT)的渐近检验程序,使用边际似然比检验(MLRT)的渐进检验程序,精确参数检验(EXPT)并应用Monte Carlo仿真来评估这些性能程序。当匹配集的数量n小到10时,两个渐近过程的估计I型误差仍可以与标称水平很好地吻合。当匹配的单元数K超过4时,由于K的增加对功率的影响通常会变得最小。当匹配集中的故障时间之间的类内相关性较小时,在一对一的配对设计中,使用CLFT通常比使用MLRT或EXPT具有更大的功效。另一方面,当匹配集内故障时间之间的类内相关性较大时,在本文考虑的几乎所有情况下,MLRT的功效都高于CLFT和EXPT的功效。因此,作者推荐MLRT。

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