scholarly journals Winsorized Modified One Step M-Estimator As a Measure of the Central Tendency in the Alexander-Govern Test

2016 ◽  
Vol 7 (3) ◽  
pp. 233
Author(s):  
Tobi Kingsley Ochuko ◽  
Suhaida Abdullah ◽  
Zakiyah Zain ◽  
Sharipah Syed Soaad Yahaya
Keyword(s):  
Type I Error ◽  
Good Control ◽  
Error Rates ◽  
Extreme Condition ◽  
Type I ◽  
Central Tendency ◽  
Central Tendency Measure ◽  
Type I Error Rates ◽  
M Estimator ◽  
One Step

This research dealt with making comparison of the independent group tests with the use of parametric technique. This test used mean as its central tendency measure. It was a better alternative to the ANOVA, the Welch test and the James test, because it gave a good control of Type I error rates and high power with ease in its calculation, for variance heterogeneity under a normal data. But the test was found not to be robust to non-normal data. Trimmed mean was used on the test as its central tendency measure under non-normality for two group condition, but as the number of groups increased above two, the test failed to give a good control of Type I error rates. As a result of this, the MOM estimator was applied on the test as its central tendency measure and is not influenced by the number of groups. However, under extreme condition of skewness and kurtosis, the MOM estimator could no longer control the Type I error rates. In this study, the Winsorized MOM estimator was used in the AG test, as a measure of its central tendency under non-normality. 5,000 data sets were simulated and analysed for each of the test in the research design with the use of Statistical Analysis Software (SAS) package. The results of the analysis shows that the Winsorized modified one step M-estimator in the Alexander-Govern (AGWMOM) test, gave the best control of Type I error rates under non-normality compared to the AG test, the AGMOM test, and the ANOVA, with the highest number of conditions for both lenient and stringent criteria of robustness. 

scholarly journals Modifying and Evaluating the Alexander-Govern Test Using Real Data

2015 ◽  
Vol 9 (12) ◽  
pp. 1
Author(s):  
Tobi Kingsley Ochuko ◽  
Suhaida Abdullah ◽  
Zakiyah Binti Zain ◽  
Sharipah Syed Soaad Yahaya
Keyword(s):  
Type I Error ◽  
Parametric Method ◽  
Real Life ◽  
Good Control ◽  
Error Rates ◽  
P Value ◽  
Type I ◽  
Central Tendency ◽  
Central Tendency Measure ◽  
Type I Error Rates

This study examines the use of independent group test of comparing two or more means by using parametric method, such as the Alexander-Govern (<em>AG</em>) test. The Alexander-Govern test is used for comparing two or more groups and is a better alternative compared to the James test, the Welch test and the <em>ANOVA</em>. This test has a good control of Type I error rates and gives a high power under variance heterogeneity for a normal data, but it is not robust for non-normal data. As a result, trimmed mean was applied on the test under non-normal data for two group condition. But this test could not control the Type I error rates, when the number of groups exceed two groups. As a result, the <em>MOM</em> estimator was introduced on the test, as its central tendency measure and is not influenced by the number of groups. But this estimator fails to give a good control of Type I error rates, under skewed heavy tailed distribution. In this study, the <em>AGWMOM </em>test was applied in Alexander-Govern test as its central tendency measure. To evaluate the capacity of the test, a real life data was used. Descriptive statistics, Tests of Normality and boxplots were used to determine the normality and non-normality of the independent groups. The results show that only the group middle is not normally distributed due extreme value in the data distribution. The results from the test statistic show that the <em>AGWMOM</em> test has a smaller p-value of 0.0000002869 that is less than 0.05, compared to the <em>AG</em> test that produced a p-value of 0.06982, that is greater than 0.05. Therefore, the <em>AGWMOM</em> test is considered to be significant, compared to the <em>AG</em> test.

scholarly journals The Modification and Evaluation of the Alexander-Govern Test in Terms of Power

2015 ◽  
Vol 9 (13) ◽  
pp. 1
Author(s):  
Tobi Kingsley Ochuko ◽  
Suhaida Abdullah ◽  
Zakiyah Binti Zain ◽  
Sharipah Soaad Syed Yahaya
Keyword(s):  
Sample Size ◽  
High Power ◽  
Type I Error ◽  
Error Rates ◽  
Robust Estimator ◽  
Type I ◽  
Central Tendency ◽  
Unequal Variance ◽  
Central Tendency Measure ◽  
Type I Error Rates

<p class="zhengwen"><span lang="EN-GB">This study centres on the comparison of independent group tests in terms of power, by using parametric method, such</span><span lang="EN-GB"> as the Alexander-Govern test. The Alexander-Govern (<em>AG</em>) test uses mean as its central tendency measure. It is a better alternative compared to the Welch test, the James test and the <em>ANOVA</em>, because it produces high power and gives good control of Type I error rates for a normal data under variance heterogeneity. But this test is not robust for a non-normal data. When trimmed mean was applied on the test as its central tendency measure under non-normality, the test was only robust for two group condition, but as the number of groups increased more than two groups, the test was no more robust. As a result, a highly robust estimator known as the <em>MOM</em> estimator was applied on the test, as its central tendency measure. This test is not affected by the number of groups, but could not control Type I error rates under skewed heavy tailed distribution. In this study, the Winsorized <em>MOM</em> estimator was applied in the <em>AG</em> test, as its central tendency measure. A simulation of 5,000 data sets were generated and analysed on the test, using the <em>SAS</em> package. The result of the analysis, shows that with the pairing of unbalanced sample size of (15:15:20:30) with equal variance of (1:1:1:1) and the pairing of unbalanced sample size of (15:15:20:30) with unequal variance of (1:1:1:36) with effect size index (<em>f</em> = 0.8), the <em>AGWMOM </em>test only produced a high power value of 0.9562 and 0.8336 compared to the <em>AG </em>test, the <em>AGMOM </em>test and the <em>ANOVA </em>respectively and the test is considered to be sufficient.</span></p>

scholarly journals Winsorized Modified One Step M-estimator in Alexander-Govern Test

2015 ◽  
Vol 9 (11) ◽  
pp. 51
Author(s):  
Tobi Kingsley Ochuko ◽  
Suhaida Abdullah ◽  
Zakiyah Binti Zain ◽  
Sharipah Soaad Syed Yahaya
Keyword(s):  
Type I Error ◽  
Parametric Method ◽  
Good Control ◽  
Error Rates ◽  
Type I ◽  
Central Tendency ◽  
Unequal Variance ◽  
Power Efficient ◽  
Type I Error Rates ◽  
Group Condition

This research centres on independent group test of comparing two or more means by using the parametric method, namely the Alexander-Govern test. The Alexander-Govern (<em>AG</em>) test uses mean as a measure of its central tendency. It is a better alternative to the Welch test, James test and the <em>ANOVA</em>, because it has a good control of Type I error rates and produces a high power efficient for a normal data under variance heterogeneity, but not for non-normal data. As a result, trimmed mean was applied on the test under non-normal data for two group condition, but as the number of groups increased above two, the test fails to be robust. Due to this, when the <em>MOM</em> estimator was applied on the test, it was not influenced by the number of groups, but failed to give a good control of Type I error rates under skewed heavy tailed distribution. In this research, the Winsorized <em>MOM</em> estimator was applied in <em>AG</em> test as a measure of its central tendency. 5,000 data sets were simulated and analysed using Statistical Analysis Software (<em>SAS</em>). The result shows that with the pairing of unbalanced sample size with unequal variance of (1:36) and the combination of both balanced and unbalanced sample sizes with both equal and unequal variances, under six group condition, for g = 0.5 and h = 0.5, for both positive and negative pairing condition, the test gives a remarkable control of Type I error rates. In overall, the <em>AGWMOM</em> test has the best control of Type I error rates, across the distributions and across the groups, compared to the <em>AG</em> test, the <em>AGMOM</em> test and the <em>ANOVA</em>.

scholarly journals The Robustness of the Modified H-Statistic in the Test of Comparing Independent Groups

2020 ◽  
pp. 1-5
Author(s):  
Suhaida Abdullah ◽  
Teh Kian Wooi ◽  
Sharipah Soaad Syed Yahaya ◽  
Zahayu Md Yusof
Keyword(s):  
Error Rate ◽  
Type I Error ◽  
Type I ◽  
Central Tendency ◽  
Test Statistic ◽  
Robust Test ◽  
Central Tendency Measure ◽  
Type I Error Rate ◽  
One Step ◽  
The Mean

The H-statistic is a robust test statistic in comparing the equality of two and more than two independent groups. This statistic is one of a good alternative to the F-statistic in the analysis of variance (ANOVA). The F-statistic is good only when the distribution of data is normal with homogeneous variances. If there is a violation of at least one of these assumptions, it affects the Type I error rate of the test. The main weakness of the F-statistic is its calculation based on the mean. The mean is well-known as a very sensitive central tendency measure with 0 breakdown point, whereas the H-statistic provides a test with fewer assumptions yet powerful. This statistic is readily adaptable to any measure of central tendency, and it appears to give reasonably good results. Hence, this paper provides a detailed study on the robustness of the H-statistic and its performance using different robust central tendency measures such that the modified one-step M (MOM) estimator and Winsorized MOM estimator. Based on the simulation study, this paper also investigates the performance of the H-statistic under various data conditions. The findings reveal that this statistic performs as well as the F-statistic under normal and homogeneous variance, yet it provides better control of Type I error rate under non-normal data or heterogeneous variances or both. Keywords: H-statistic; robust test; mean; modified one-step M-estimator

scholarly journals Control of Type I Error Rates in Bayesian Sequential Designs

2019 ◽  
Vol 14 (2) ◽  
pp. 399-425 ◽  
Author(s):  
Haolun Shi ◽  
Guosheng Yin
Keyword(s):  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Sequential Designs ◽  
Type I Error Rates

scholarly journals Sisvar: a Guide for its Bootstrap procedures in multiple comparisons

2014 ◽  
Vol 38 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Daniel Furtado Ferreira
Keyword(s):  
Scientific Community ◽  
Type I Error ◽  
Multiple Comparisons ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Statistical Analysis System ◽  
Scientific Results ◽  
Analysis System ◽  
Multiple Comparison Procedures

Sisvar is a statistical analysis system with a large usage by the scientific community to produce statistical analyses and to produce scientific results and conclusions. The large use of the statistical procedures of Sisvar by the scientific community is due to it being accurate, precise, simple and robust. With many options of analysis, Sisvar has a not so largely used analysis that is the multiple comparison procedures using bootstrap approaches. This paper aims to review this subject and to show some advantages of using Sisvar to perform such analysis to compare treatments means. Tests like Dunnett, Tukey, Student-Newman-Keuls and Scott-Knott are performed alternatively by bootstrap methods and show greater power and better controls of experimentwise type I error rates under non-normal, asymmetric, platykurtic or leptokurtic distributions.

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