When the variance of a single population needs to be assessed, the well-known chi-squared test of variance is often used but relies heavily on its normality assumption. For non-normal populations, few alternative tests have been developed to conduct left tailed hypothesis tests of variance. This thesis outlines a method for generating new test statistics using a saddlepoint approximation. Several novel test statistics are proposed. The type-I error rates and power of each test are evaluated using a Monte Carlo simulation study. One of the proposed test statistics, R_gamma2, controls type-I error rates better than existing tests, while having comparable power. The only observed limitation is for populations that are highly skewed with heavy-tails, for which all tests under consideration performed poorly.
| Identifer | oai:union.ndltd.org:unf.edu/oai:digitalcommons.unf.edu:etd-1679 |
| Date | 01 January 2016 |
| Creators | Grimes, Tyler L |
| Publisher | UNF Digital Commons |
| Source Sets | University of North Florida |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | UNF Theses and Dissertations |
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