Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/24959
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | McNicholas, Paul | - |
dc.contributor.author | Pocuca, Nikola | - |
dc.date.accessioned | 2019-10-04T19:33:52Z | - |
dc.date.available | 2019-10-04T19:33:52Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://hdl.handle.net/11375/24959 | - |
dc.description.abstract | In the last few years, telemetric data arising from embedded vehicle sensors brung an overwhelming abundance of information to companies. There is no indication that this will be abated in future. This information concerning driving behaviour brings an opportunity to carry out analysis. The merging of telemetric data and informatics gives rise to a sub-field of data science known as telematics. This work encompasses matrix variate and kernel density methods for the purposes of analysing telemetric data. These methods expand the current literature by alleviating the issues that arise with high-dimensional data. | en_US |
dc.language.iso | en | en_US |
dc.subject | telematics, matrix variate | en_US |
dc.title | Matrix Variate and Kernel Density Methods for Applications in Telematics | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
NikPocucaMsc.pdf | 4.4 MB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.