Calculating Kendall's Tau with multiple measurements
| dc.contributor.advisor | Sterba-Boatwright, Blair | |
| dc.contributor.advisor | Sterba-Boatwright, Blair | |
| dc.contributor.author | Geller, Adam | |
| dc.contributor.author | Geller, Adam | |
| dc.contributor.committeeMember | Jin, Lei | |
| dc.contributor.committeeMember | Guardiola, Jose | |
| dc.contributor.committeeMember | Jin, Lei | |
| dc.contributor.committeeMember | Guardiola, José | |
| dc.date.accessioned | 2018-06-21T15:05:49Z | |
| dc.date.available | 2018-06-21T15:05:49Z | |
| dc.date.issued | 2018-05 | |
| dc.date.issued | 2018-05 | |
| dc.description.abstract | Relationships between time series of environmental variables are commonly calculated using non-parametric methods, such as Kendall’s τ, because of ”non-detects”, i.e., left-censored data that falls below a measurement limit. However, these methods are not well-adapted to situations where variables have multiple contemporaneous measurements. In this thesis, we define a new method, τ ̃, in an attempt to calculate correlations using each of the multiple measurements instead of daily means. We investigate τ ̃ using two methods: simulations that approximate a null distribution for τ ̃ and closed form calculations for a specific special case. We also apply τ ̃ to an actual data set. The results of our investigation shows that τ ̃ may handle certain things, such as outliers, better than current methods. However, its requirements for distributional assumptions about the data make it a less practical option for real data. Further work could explore ways to avoid the prerequisite need for distribution knowledge and could also further investigate τ ̃ under noise sampled from asymmetric distributions. | en_US |
| dc.description.college | College of Science and Engineering | en_US |
| dc.description.department | Mathematics and Statistics | en_US |
| dc.format.extent | 28 pages | en_US |
| dc.identifier.uri | http://hdl.handle.net/1969.6/31357 | |
| dc.identifier.uri | http://hdl.handle.net/1969.6/31357 | |
| dc.language.iso | en_US | en_US |
| dc.rights | This material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher. | en_US |
| dc.subject | Correlation | en_US |
| dc.subject | Kendall | en_US |
| dc.subject | Rank | en_US |
| dc.subject | Tau | en_US |
| dc.title | Calculating Kendall's Tau with multiple measurements | en_US |
| dc.type | Text | en_US |
| dc.type.genre | Thesis | en_US |
| thesis.degree.discipline | Mathematics | en_US |
| thesis.degree.grantor | Texas A & M University--Corpus Christi | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |