Power Comparison of Some Goodness-of-fit Tests

• Main Author: Liu, Tianyi
• Format: Others
• Published: FIU Digital Commons 2016
• Subjects: Goodness-of-fit test; Exponential distribution; Power comparison; Monte-Carlo simulation; Statistical Methodology
• Online Access: http://digitalcommons.fiu.edu/etd/2572; http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=3783&context=etd

There are some existing commonly used goodness-of-fit tests, such as the Kolmogorov-Smirnov test, the Cramer-Von Mises test, and the Anderson-Darling test. In addition, a new goodness-of-fit test named G test was proposed by Chen and Ye (2009). The purpose of this thesis is to compare the performance of some goodness-of-fit tests by comparing their power. A goodness-of-fit test is usually used when judging whether or not the underlying population distribution differs from a specific distribution. This research focus on testing whether the underlying population distribution is an exponential distribution. To conduct statistical simulation, SAS/IML is used in this research. Some alternative distributions such as the triangle distribution, V-shaped triangle distribution are used. By applying Monte Carlo simulation, it can be concluded that the performance of the Kolmogorov-Smirnov test is better than the G test in many cases, while the G test performs well in some cases.