Functional and High-dimensional Data Analysis: New Directions and Innovations
Category: International Statistical Institute
Abstract
In this session, we gather four promising statisticians from various research areas and three different countries (Israel, Canada, U.S.A.) to discuss newly merged functional and high-dimensional data. Specifically, the four speakers will present functional data classification based on the deep learning method and reproducing kernel Hilbert space framework. They will show that the proposed classifiers can achieve upper bound of the minimax optimality under mild assumptions. Furthermore, continuous-time multivariate analysis and penalized empirical likelihood with the sparse cox regression model will also be discussed for complex high-dimensional data analysis.