报告简介  Abstract

Functional data analysis concerns a sample of random functions, such as a collection of body growth trajectories. Dimension reduction tools, such as functional principal component analysis, are available to reduce and represent the infinite-dimensional functions. In this work, we are interested in estimating densities as functions, where each density comes from a subpopulation. For example, in the context of epidemiology, the age distributions of patients with different diseases is of central interest, where the disease defines a subpopulation. A key challenge comes from the highly variable sample sizes for different conditions, making the estimation of age profiles difficult for rare conditions. We propose a fully data-driven approach to estimate the densities without the need of specifying the parametric form of the density families. The idea is to map the density functions to a Hilbert space and then apply functional data analytic methods so as to derive low-dimensional approximates. I will show that the proposed methods yield interpretable results and are efficient in applications to electronic medical record and rainfall data.

嘉宾简介  About the Speaker

Xiongtao Dai is an Assistant Professor in the Department of Statistics at Iowa State University. He grew up in Shenzhen and studied at Shenzhen Middle School before heading to higher education. He obtained his undergraduate degree from the University of Hong Kong and the PhD degree University of California, Davis. Xiongtao has keen research interests in developing methods and theory for functional, longitudinal, and geometrical data. He also works on statistical applications such as remote sensing, plant genomics, agronomy, and neuroscience.

讲座海报Poster