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. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24959 |
| Date | January 2019 |
| Creators | Pocuca, Nikola |
| Contributors | McNicholas, Paul, Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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