Title: Fast and fair simultaneous confidence bands for functional parameters
Authors: Dominik Liebl - University Bonn (Germany) [presenting]
Matthew Reimherr - Pennsylvania State University (United States)
Abstract: Quantifying uncertainty using confidence regions is a central goal of statistical inference. Despite this, methodologies for confidence bands in Functional Data Analysis are underdeveloped compared to estimation and hypothesis testing. A major leap forward in this area is made by presenting a new methodology for constructing simultaneous confidence bands for functional parameter estimates. These bands possess a number of striking qualities: (1) they have a nearly closed-form expression, (2) they give nearly exact coverage, (3) they have a finite sample correction, (4) they do not require an estimate of the full covariance of the parameter estimate, and (5) they can be constructed adaptively according to a desired criterion. One option for choosing bands we find especially interesting is the concept of fair bands, where breaches in coverage are equally likely to occur on any two subintervals of the same length, which could be especially useful in longitudinal studies over long time scales. These bands are constructed by integrating and extending tools from Random Field Theory, an area that has yet to overlap with Functional Data Analysis.