Abstract

Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.

摘要

功能数据正迅速成为众多行业的主要信息来源。功能数据的一个特别具有挑战性的方面是不确定性。在这个独特的案例研究中，我们介绍了在Sandia国家实验室（SNL）为选定的应用程序创建边界函数的尝试。首次尝试涉及功能主成分分析（fPCA）的简单扩展，以合并协变量。尽管此方法很简单，但是扩展受到边界曲线覆盖率差的困扰。这导致了第二次尝试使用弹性方法，从而以更复杂的代价获得了更准确的覆盖范围。