Date: 2015-01-15

Time: 16:00-17:00

Location: CRM 1360 (U. de Montréal)

Abstract:

Functional data analysis (FDA) has received substantial attention, 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, e.g., smoothness constraints. The estimation and inference procedures usually do not depend on merely a finite number of parameters, which contrasts with parametric models, and exploit techniques, such as smoothing methods and dimension reduction, that allow data to speak for themselves. In this talk, I will give an overview of FDA methods and related topics developed in recent years.

Speaker

Fang Yao is a Professor in the Department of Statistics at the University of Toronto. He is the 2014 recipient of the CRM-SSC Prize.