Nonparametric Estimation of Repeated Densities with Heterogeneous Sample Sizes
Date: Thursday, 7 October 2021
Time: 10 am - 11 am
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 each 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 for modeling electronic medical records and extreme rainfall.
Zoom Link: Please contact Yanrong Yang (yanrong.yang@anu.edu.au) to obtain the zoom link for this seminar.