Various approaches for discriminant analysis of longitudinal data are investigated,
with some focus on model-based approaches. The latter are typically based on the
modi ed Cholesky decomposition of the covariance matrix in a Gaussian mixture;
however, non-Gaussian mixtures are also considered. Where applicable, the Bayesian
information criterion is used to select the number of components per class. The
various approaches are demonstrated on real and simulated data. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/22317 |
| Date | January 2017 |
| Creators | Matira, Kevin |
| Contributors | McNicholas, Paul, Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0022 seconds