Adaptive and automatic trimming in testing the equality of two group case

• Main Authors: Suhaida, Abdullah, Yahaya Sharipah Soaad Syed, Othman Abdul Rahman
• Format: Conference Proceeding
• Language: English
• Subjects: Normality; Outliers (statistics); Robustness; Skewed distributions; Trimming
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/1.4882599

In testing the equality of two independent groups, t-test plays a very important role for the purpose. This test is reliable when the data is normally distributed. Based on central limit theorem, the normality assumption is fulfilled with large data set, but getting large data set is not always feasible. Most of the time, the researchers have to make do with small sample sizes which are hardly normally distributed. There are many causes of non normality, and one of it is the presence of outliers. One way to handle outliers is by using robust estimator with trimming approach. In this study, robust estimators using different trimming approaches namely adaptive and automatic trimming were proposed as the center measures in Alexander-Govern (AG) test. The results of the Type I error rate was then compared with the original AG test and the classical t-test. The AG test with the adaptive and automatic trimming showed robustness across distributions. The two trimming approaches are comparable to each other in most conditions. As expected the original AG test and classical t-test cannot maintain their robustness especially under skewed distribution.