The Experts below are selected from a list of 2346519 Experts worldwide ranked by ideXlab platform
Tobi Kingsley Ochuko - One of the best experts on this subject based on the ideXlab platform.
-
Winsorized Modified One Step M-Estimator As a Measure of the Central Tendency in the Alexander-Govern Test
ComTech: Computer Mathematics and Engineering Applications, 2016Co-Authors: Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, Sharipah Syed Soaad YahayaAbstract: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.
-
Winsorized Modified One Step M-Estimator in Alexander-Govern Test
2016Co-Authors: Tobi Kingsley Ochuko, Syed Yahaya, Sharipah Soaad, Suhaida Abdullah, Zakiyah Binti Zain, Syed YahayaAbstract: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 (AG) test uses mean as a measure of its central tendency. It is a better alternative to the Welch test, James test and the ANOVA, 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 MOM 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 MOM estimator was applied in AG test as a measure of its central tendency. 5,000 data sets were simulated and analysed using Statistical Analysis Software (SAS). 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 AGWMOM test has the best control of Type I error rates, across the distributions and across the groups, compared to the A
-
The Power of the Test for the Winsorized Modified Alexander-Govern Test
ComTech: Computer Mathematics and Engineering Applications, 2016Co-Authors: Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, Sharipah Syed Soaad YahayaAbstract: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.
-
Winsorized modified one step M-Estimator in Alexander-Govern test
Modern Applied Science, 2015Co-Authors: Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, Sharipah Soaad Syed YahayaAbstract: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 (AG) test uses mean as a measure of its central tendency. It is a better alternative to the Welch test, James test and the ANOVA, 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 MOM 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 MOM estimator was applied in AG test as a measure of its central tendency. 5,000 data sets were simulated and analysed using Statistical Analysis Software (SAS). 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 AGWMOM test has the best control of Type I error rates, across the distributions and across the groups, compared to the AG test, the AGMOM test and the ANOVA.
Syed Yahaya, Sharipah Soaad - One of the best experts on this subject based on the ideXlab platform.
-
Winsorized Modified One Step M-Estimator in Alexander-Govern Test
2016Co-Authors: Tobi Kingsley Ochuko, Syed Yahaya, Sharipah Soaad, Suhaida Abdullah, Zakiyah Binti Zain, Syed YahayaAbstract: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 (AG) test uses mean as a measure of its central tendency. It is a better alternative to the Welch test, James test and the ANOVA, 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 MOM 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 MOM estimator was applied in AG test as a measure of its central tendency. 5,000 data sets were simulated and analysed using Statistical Analysis Software (SAS). 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 AGWMOM test has the best control of Type I error rates, across the distributions and across the groups, compared to the A
-
Using real life data to validate the winsorized modified Alexander-Govern test
Scientific & Academic Publishing. All Rights Reserved, 2016Co-Authors: Ochuko, Tobi Kingsley, Abdullah Suhaida, Zain Zakiyah, Syed Yahaya, Sharipah SoaadAbstract:To evaluate the efficiency and reliability of the Alexander-Govern (AG) test and the Winsorized Modified One Step M-Estimator in the Alexander-Govern (AGWMOM) test, using real life data. Methods: Test of homogeneity of variance was done from real life data, comprising of young, middle and old groups, using the Levene’s test to see if the three groups are different from each other or not as the reaction time changes.Descriptive statistics, Test of normality and Test Statistic were performed for the three independent groups, to evaluate the reliability and efficiency of the tests. Results: The p-value from the test of homogeneity of the variance is greater than 0.05, i.e 0.174 > 0.05 and it shows that we accept HO and conclude that there is no difference between the groups as the reaction time changes.The descriptive statistics show that the AGWMOM test has a smaller standard error compared to the AG test.The result of the test statistic reveals that the AGWMOM test produced a p-value of 0.0000002869 that is considered to be significant compared to the AG test that produced a p-value of 0.0698 that is regarded as not significant, since its p-value is > 0.05. Conclusions: The AGWMOM test is more efficient and reliable in minimizing error as much as possible from the real life data, because the test produced a smaller standard error from the real life data in comparison to the AG test and is regarded as significant
-
H-statistic with winsorized modified One-Step M-Estimator for two independent groups design
'AIP Publishing', 2014Co-Authors: Ong, Gie Xao, Syed Yahaya, Sharipah Soaad, Abdullah Suhaida, Md Yusof ZahayuAbstract:Two-sample independent t-test is a classical method which is widely used to test the equality of two groups.However, this test is easily affected by any deviation in normality, more obvious when heterogeneity of variances and group sizes exist.It is well known that the violation in the assumption of these tests will lead to inflation in Type I error rate and depression in statistical test power.In mitigating the problem, robust methods can be used as alternatives.One such method is H-statistic.When used with modified One-Step M-Estimator (MOM), this test statistic (MOM-H) produce good control of Type I error even under small sample size but inconsistent across certain conditions investigated.Furthermore, power of the test is low which might be due to the trimming process.In this study, MOM is winsorized (WMOM) to sustain the original sample size.The H-statistic with WMOM as the central tendency measures (denoted as WMOM-H) showed better control of Type I error as compared to MOM-H especially under balance design regardless of the shapes of distribution investigated in the study.It also performed well under highly skewed and heavy tailed distribution for unbalanced design. In general, this study demonstrated that winsorization process (WMOM) could improve the performance of H-statistic in terms of Type I error rate control
Sharipah Syed Soaad Yahaya - One of the best experts on this subject based on the ideXlab platform.
-
Winsorized Modified One Step M-Estimator As a Measure of the Central Tendency in the Alexander-Govern Test
ComTech: Computer Mathematics and Engineering Applications, 2016Co-Authors: Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, Sharipah Syed Soaad YahayaAbstract: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.
-
The Power of the Test for the Winsorized Modified Alexander-Govern Test
ComTech: Computer Mathematics and Engineering Applications, 2016Co-Authors: Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, Sharipah Syed Soaad YahayaAbstract: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.
Suhaida Abdullah - One of the best experts on this subject based on the ideXlab platform.
-
Winsorized Modified One Step M-Estimator As a Measure of the Central Tendency in the Alexander-Govern Test
ComTech: Computer Mathematics and Engineering Applications, 2016Co-Authors: Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, Sharipah Syed Soaad YahayaAbstract: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.
-
Winsorized Modified One Step M-Estimator in Alexander-Govern Test
2016Co-Authors: Tobi Kingsley Ochuko, Syed Yahaya, Sharipah Soaad, Suhaida Abdullah, Zakiyah Binti Zain, Syed YahayaAbstract: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 (AG) test uses mean as a measure of its central tendency. It is a better alternative to the Welch test, James test and the ANOVA, 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 MOM 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 MOM estimator was applied in AG test as a measure of its central tendency. 5,000 data sets were simulated and analysed using Statistical Analysis Software (SAS). 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 AGWMOM test has the best control of Type I error rates, across the distributions and across the groups, compared to the A
-
The Power of the Test for the Winsorized Modified Alexander-Govern Test
ComTech: Computer Mathematics and Engineering Applications, 2016Co-Authors: Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, Sharipah Syed Soaad YahayaAbstract: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.
-
Winsorized modified one step M-Estimator in Alexander-Govern test
Modern Applied Science, 2015Co-Authors: Tobi Kingsley Ochuko, Suhaida Abdullah, Zakiyah Zain, Sharipah Soaad Syed YahayaAbstract: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 (AG) test uses mean as a measure of its central tendency. It is a better alternative to the Welch test, James test and the ANOVA, 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 MOM 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 MOM estimator was applied in AG test as a measure of its central tendency. 5,000 data sets were simulated and analysed using Statistical Analysis Software (SAS). 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 AGWMOM test has the best control of Type I error rates, across the distributions and across the groups, compared to the AG test, the AGMOM test and the ANOVA.
Bent Nielsen - One of the best experts on this subject based on the ideXlab platform.
-
an analysis of the indicator saturation estimator as a robust regression estimator
CREATES Research Papers, 2008Co-Authors: Soren Johansen, Bent NielsenAbstract:An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of finding an estimator that is robust to outliers and structural breaks. This estimator is an example of a One-Step M-Estimator based on Huber's skip function. The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques. Stationary processes, trend stationary autoregressions and unit root processes are considered.
-
an analysis of the indicator saturation estimator as a robust regression
Research Papers in Economics, 2008Co-Authors: Soren Johansen, Bent NielsenAbstract:An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of finding an estimator that is robust to outliers and structural breaks. This estimator is an example of a One-Step M-Estimator based on Huber's skip function. The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques. Stationary processes, trend stationary autoregressions and unit root processes are considered. Classification JEL: C32
