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

2016 ◽  
Vol 7 (4) ◽  
pp. 307
Author(s):  
Tobi Kingsley Ochuko ◽  
Suhaida Abdullah ◽  
Zakiyah Zain ◽  
Sharipah Syed Soaad Yahaya
Keyword(s):  
Type I Error ◽  
Parametric Method ◽  
Type I ◽  
Analysis Software ◽  
Group Case ◽  
Power Of The Test ◽  
M Estimator ◽  
One Step ◽  
Irregular Data ◽  
Statistical Analysis Software

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 isineffective 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 providesexcellence 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.

scholarly journals Comparing the "typical score" across independent groups based on different criteria for trimming

2006 ◽  
Vol 3 (1) ◽  
Author(s):  
Sharipah Syed Yahaya ◽  
Abdul Othman ◽  
Harvey Keselman
Keyword(s):  
Treatment Group ◽  
Least Squares ◽  
Error Control ◽  
Type I Error ◽  
Bootstrap Method ◽  
Null Distribution ◽  
Type I ◽  
M Estimator ◽  
One Step ◽  
Trimmed Means

Nonnormality and variance heterogeneity affect the validity of the traditional tests for treatment group equality (e.g. ANOVA F-test and t-test), particularly when group sizes are unequal. Adopting trimmed means instead of the usual least squares estimator has been shown to be mostly affective in combating the deleterious effects of nonnormality. There are, however, practical concerns regarding trimmed means, such as the predetermined amount of symmetric trimming that is typically used. Wilcox and Keselman proposed the Modified One- Step M-estimator (MOM) which empirically determines the amount of trimming. Othman et al. found that when this estimator is used with Schrader and Hettmansperger's H statistic, rates of Type I error were well controlled even though data were nonnormal in form. In this paper, we modified the criterion for choosing the sample values for MOM by replacing the default scale estimator, MADn, with two robust scale estimators, Sn and Tn , suggested by Rousseeuw and Croux (1993). To study the robustness of the modified methods, conditions that are known to negatively affect rates of Type I error were manipulated. As well, a bootstrap method was used to generate a better approximate sampling distribution since the null distribution of MOM-H is intractable. These modified methods resulted in better Type I error control especially when data were extremely skewed.

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. 

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

scholarly journals On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data

2020 ◽  
Vol 12 (6) ◽  
pp. 1008 ◽  
Author(s):  
Ana Militino ◽  
Mehdi Moradi ◽  
M. Ugarte
Keyword(s):  
Error Probability ◽  
Type I Error ◽  
Remote Sensing Data ◽  
Change Point Detection ◽  
Change Points ◽  
Detection Methods ◽  
Type I ◽  
Power Of The Test ◽  
Type I Error Probability ◽  
Point Detection

Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E.divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann–Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann–Kendall test is generally the preferable choice. Although Mann–Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018.

scholarly journals An efficient genome-wide association test for mixed binary and continuous phenotypes with applications to substance abuse research

2016 ◽  
Vol 27 (3) ◽  
pp. 905-919
Author(s):  
Anne Buu ◽  
L Keoki Williams ◽  
James J Yang
Keyword(s):  
Type I Error ◽  
Empirical Distribution ◽  
Genetic Effect ◽  
Association Test ◽  
Genome Wide Association ◽  
Type I ◽  
Multiple Phenotypes ◽  
Power Of The Test ◽  
Genome Wide ◽  
The One

We propose a new genome-wide association test for mixed binary and continuous phenotypes that uses an efficient numerical method to estimate the empirical distribution of the Fisher’s combination statistic under the null hypothesis. Our simulation study shows that the proposed method controls the type I error rate and also maintains its power at the level of the permutation method. More importantly, the computational efficiency of the proposed method is much higher than the one of the permutation method. The simulation results also indicate that the power of the test increases when the genetic effect increases, the minor allele frequency increases, and the correlation between responses decreases. The statistical analysis on the database of the Study of Addiction: Genetics and Environment demonstrates that the proposed method combining multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests.

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.

Phase II study of dovitinib in first-line metastatic or nonresectable primary adrenocortical carcinoma (ACC): SOGUG study 2011-03.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. TPS4688-TPS4688
Author(s):  
Paula Jimenez ◽  
Marta Guix ◽  
Nuria Lainez Milagro ◽  
Luis Leon Mateos ◽  
Maria Jose Mendez Vidal ◽  
...  
Keyword(s):  
Phase Ii ◽  
Type I Error ◽  
Sample Size Calculation ◽  
Growth Factor Receptor ◽  
Collaborative Group ◽  
Type I ◽  
Open Label ◽  
Prior Therapy ◽  
Power Of The Test ◽  
Open Label Phase

TPS4688 Background: Dovitinib is a novel targeted therapy, that has proven to inhibit, among other tyrosin kinases, the fibroblast growth factor receptor (FGFR). Since this pathway has been proposed to play a major role in ACC, we aim to test the clinical efficacy of dovitinib in this tumor. Methods: An open label phase II trial has been designed in patients with advanced non-resectable ACC. The objective will be to obtain at least a 15% response rate according to RECIST criteria. Taking as a basis the two-stage Gehan model, 15 patients would need to be included in the first stage to demonstrate a treatment efficacy of at least 15%. Sample size calculation was done based on the following parameters, probability of Type I error α = 0.05, power of the test (1 - β) = 0.8. Main inclusion criteria are advanced non-resectable disease and no prior therapy (other than mitotane). Dovitinib scheduled dose matches currently employed standard in the drug development (500mg daily for 5 days then 2 days off) for 6 months. If clinical benefit is obtained longer treatment will be allowed for particular patients. Since this is an extremely unfrequent disease 7 institutions, members of the SOGUG (Spanish Oncology Genitourinary Group), will participate. The active support of a big collaborative group will guarantee candidate patients to be refereed to such institutions. Starting January 26th 2012 recruitment is scheduled to last around 12 months. A translational research, including whole exome analysis, will be performed in order to improve our scarce knowledge of ACC.

scholarly journals SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jiaqiang Zhu ◽  
Shiquan Sun ◽  
Xiang Zhou
Keyword(s):  
Error Control ◽  
Type I Error ◽  
Expression Patterns ◽  
Parametric Method ◽  
Parametric Modeling ◽  
Type I ◽  
Cell Type ◽  
Robust Detection ◽  
Transcriptomic Studies ◽  
Non Parametric

AbstractSpatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.

scholarly journals Comparison of Some Robust Wilks’ Statistics for the One-Way Multivariate Analysis of Variance (MANOVA )

2019 ◽  
Vol 11 (2) ◽  
pp. 42-58
Author(s):  
Abdullah A. Ameen ◽  
Osama H. Abbas
Keyword(s):  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Test Statistics ◽  
Multivariate Analysis Of Variance ◽  
Power Of The Test ◽  
Monte Carlo Studies ◽  
The One

The classicalWilks' statistic is mostly used to test hypothesesin the one-way multivariate analysis of variance (MANOVA), which is highly sensitive to the effects of outliers. The non-robustness of the test statistics based on normal theory has led many authors to examine various options.In this paper, we presented a robust version of the Wilks' statistic and constructed its approximate distribution.A comparison was made between the proposed statistics and some Wilks' statistics. The Monte Carlo studies are used to obtain performance assessment of test statistics in different data sets.Moreover, the results of the type I error rate and the power of test were considered as statistical tools to compare test statistics.The study reveals that, under normally distributed, the type I error rates for the classical and the proposedWilks' statistics are close to the true significance levels, and the power of the test statistics are so close. In addition, in the case of contaminated distribution, the proposed statistic is the best.  

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>.

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