Phd Oral Presentation
Functional data are getting prevalent in many research and industrial fields in recent decades. It is often of interest to classify functional data properly. A number of supervised classification methods have been proposed in the literature. In this thesis, I propose and study three new classifiers for supervised classification for functional data, including a supervised classification method based on functional cosine similarity, a new k-nearest neighbour classifier, and an inverse distance based classifier. Intensive simulation studies and a number of real functional data examples are conducted to demonstrate and illustrate the good performance of the three new functional classifiers via comparing them against several existing competitors.