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Simultaneous Tests For Patterned Recognition Using Nonparametric Partially Sequential Procedure

机译:使用非参数部分顺序过程进行模式识别的同时测试

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In the present paper we introduce a partially sequential sampling procedure to develop a nonparametric method for simultaneous testing. Our work, as in [U. Bandyopadhyay, A. Mukherjee, B. Purkait, Nonparametric partial sequential tests for patterned alternatives in multi-sample problems, Sequential Analysis 26 (4) (2007) 443-466], is motivated by an interesting investigation related to arsenic contamination in ground water. Here we incorporate the idea of multiple hypotheses testing as in [Y. Benjamini, T. Hochberg, Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of Royal Statistical Society B 85 (1995)289-300] in a typical way. We present some Monte Carlo studies related to the proposed procedure. We observe that the proposed sampling design minimizes the expected sample sizes in different situations. The procedure as a whole effectively describes the testing under dual pattern alternatives. We indicate in brief some large sample situations. We also present detailed analysis of a geological field survey data.
机译:在本文中,我们介绍了部分顺序的采样过程,以开发用于同步测试的非参数方法。我们的工作,如[U. Bandyopadhyay,A。Mukherjee,B。Purkait,针对多样本问题中带图案替代品的非参数部分顺序测试,Sequential Analysis 26(4)(2007)443-466],其动机是与地下水中砷污染有关的有趣研究。在这里,我们结合了[Y. Benjamini,T. Hochberg,“控制错误发现率:一种实用且强大的多重测试方法”,《皇家统计学会杂志》 B 85(1995)289-300]。我们提出一些与拟议程序有关的蒙特卡洛研究。我们观察到,建议的抽样设计可以最大程度地减少不同情况下的预期抽样规模。整个过程有效地描述了双模式替代方案下的测试。我们简要指出一些大样本情况。我们还介绍了地质调查数据的详细分析。

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