B0375
Title: Sensible functional linear discriminant analysis
Authors: Ci-Ren Jiang - National Taiwan University (Taiwan) [presenting]
Lu-Hung Chen - National Chung Hsing University (Taiwan)
Abstract: The aim is to extend Fisher's linear discriminant analysis (LDA) to both densely recorded functional data and sparsely observed longitudinal data for general c-category classification problems. We propose an efficient approach to identify the optimal LDA projections in addition to managing the nonivertibility issue of the covariance operator emerging from this extension. A conditional expectation technique is employed to tackle the challenge of projecting sparse data to the LDA directions. We study the asymptotic properties of the proposed estimators and show that the asymptotically perfect classification can be achieved under certain circumstances. The performance of this new approach is further demonstrated with numerical examples.
