The Power of the Test for the Winsorized Modified Alexander-Govern Test

ComTech Pub Date : 2016-07-25 DOI:10.21512/COMTECH.V7I4.3764
Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, S. S. S. Yahaya
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Abstract

This research examined the usage of the parametric method in comparing two or more means as independent group test, for instance, the Alexander-Govern (AG) test. The utilization of mean as the determinant for the center of distribution of variance diversity takes place in testing, and the test provides excellence in maintaining the amount of Type I error and giving immense sensitivity for a regular data. Unfortunately, it is ineffective on irregular data, leading to the application of trimmed mean upon testing as the determinant for the center of distribution under irregular data for two group condition. However, as the group quantity is more than two, the estimator unsuccessfully provides excellence in maintaining the amount of Type I error. Therefore, an estimator high in effectiveness called the MOM estimator was introduced for the testing as the determinant for the center of distribution. Group quantity in a test does not affect the estimator, but it unsuccessfully provides excellence in maintaining the amount of Type I error under intense asymmetry and unevenness. The application of Winsorized modified one-step M-estimator (WMOM) upon the Alexander-Govern testing takes place so that it can prevail against its drawbacks under irregular data in the presence of variance diversity, can eliminate the presence of the outside observation and can provide effectiveness for the testing on irregular data. Statistical Analysis Software (SAS) was used for the analysis of the tests. The results show that the AGWMOM test gave the most intense sensitivity under g = 0,5 and h = 0,5, for four group case and g = 0 and h = 0, under six group case, differing from three remaining tests and the sensitivity of the AG testing is said suffices and intense enough.
winsorization Modified Alexander-Govern Test的检验能力
本研究检验了参数方法在比较两个或多个均值作为独立组检验时的使用,例如,Alexander-Govern (AG)检验。利用均值作为方差多样性分布中心的行列式发生在检验中,该检验在保持I型误差的数量和对常规数据的巨大灵敏度方面提供了卓越的性能。不幸的是,它对不规则数据是无效的,导致在两组条件下不规则数据的分布中心的决定因素是在检验后的裁剪均值。然而,由于组数量大于两个,估计器无法在保持第一类误差的数量方面提供卓越。因此,引入了一种高效的估计量——MOM估计量作为分布中心的行列式进行检验。测试中的组数量不影响估计量,但在强烈的不对称和不均匀情况下,它不能成功地提供保持I型误差量的卓越性。将Winsorized modified one-step M-estimator (WMOM)应用于Alexander-Govern检验,克服了在存在方差多样性的不规则数据下存在的缺点,消除了外部观测的存在,为不规则数据的检验提供了有效性。采用统计分析软件(SAS)对试验进行分析。结果表明,AGWMOM试验在g = 0,5和h = 0,5条件下灵敏度最高,在4组g = 0和h = 0条件下灵敏度最高,在6组g = 0和h = 0条件下灵敏度最高。
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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审稿时长
16 weeks
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