Document Type

Thesis

Degree

Master of Science (MS)

Department

Statistics

First Advisor's Name

Zhenmin Chen

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Gauri Ghai

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Florence George

Third Advisor's Committee Title

Committee member

Keywords

Goodness-of-fit test, Exponential distribution, Power comparison, Monte-Carlo simulation

Date of Defense

7-6-2016

Abstract

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.

Identifier

FIDC000750

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