A depth-based global envelope test for comparing two groups of functions with applications to biomedical data

Functional data are commonly observed in many emerging biomedical fields and their analysis is an exciting developing area in statistics. Numerous statistical methods, such as principal components, analysis of variance, and linear regression, have been extended to functional data. The statistical analysis of functions can be significantly improved using nonparametric and robust estimators. New ideas of depth for functional data have been proposed in recent years and can be extended to image data. They provide a way of ordering curves or images from center-outward, and of defining robust order statistics in a functional context. In this paper we develop depth-based global envelope tests for comparing two groups of functions or images. In addition to providing global P-values, the proposed envelope test can be displayed graphically and indicates the specific portion(s) of the functional data (eg, in pixels or in time) that may have led to rejection of the null hypothesis. We show in a simulation study the performance of the envelope test in terms of empirical power and size in different scenarios. The proposed depth-based global approach has good power even for small differences and is robust to outliers. The methodology introduced is applied to test whether children with normal and low birth weight have similar growth pattern. We also analyzed a brain image dataset consisting of positron emission tomography scans of severe depressed patients and healthy controls. The global envelope test was used to find and visualize differences between the two groups.