A0195
Title: Functional data analysis and related topics
Authors: Fang Yao - Peking University, University of Toronto (China) [presenting]
Abstract: Functional data analysis (FDA) has received substantial attention in recent years, with applications arising from various disciplines, such as engineering, public health, finance etc. In general, the FDA approaches focus on nonparametric underlying models that assume the data are observed from realizations of stochastic processes satisfying some regularity conditions. The estimation and inference procedures usually do not depend on just a finite number of parameters, which contrasts with parametric models, and exploit techniques such as smoothing methods, dimension reduction that allow data to speak for themselves. This tutorial will cover general ideas in functional data analysis, such as functional principal component analysis, basis representation models, functional linear regression as well as more flexible regression type models, and so on. Some basic computing and data analysis using R and/or Matlab will be also introduced.
