PhD Thesis Proposal - Public Lecture - Yuanhao Lai

Student: Yuanhao Lai

Title: Ensemble Quantile Classifier

Abstract:  The median-based classifier and the quantile-based classifier are useful for discriminating high-dimensional data with heavy-tailed or skewed inputs. The ensemble quantile classifier provides a more flexible regularized classifier that can outperform these classifiers especially with high-dimensional data, asymmetric data, or when there are many irrelevant extraneous inputs. The improved performance is demonstrated by a simulation study as well as an application to text categorization. Asymptotic analysis presented in the Appendix shows that this method will provide the Bayes' error rate under suitable general model assumptions. An R package implementing the method discussed in this paper is freely available.

Supervisor: Ian McLeod