PROFIT: projection-based test in longitudinal functional data

Abstract

In many modern applications, a dependent functional response is observed for each subject over repeated time, leading to longitudinal functional data. In this paper, we propose a novel statistical procedure to test whether the mean function varies over time. Our approach relies on reducing the dimension of the response using data-driven orthogonal projections, and employs likelihood-based hypothesis testing. We investigate the methodology theoretically and discuss a computationally efficient implementation. The proposed test maintains the Type-1 error rate, and shows excellent power to detect departures from the null hypothesis in finite sample simulation studies. We apply our method to the longitudinal diffusion tensor imaging study of multiple sclerosis (MS) patients to formally assess whether the brain's healthy tissue, as summarised by the fractional anisotropy (FA) profile, degrades over time during the study period.

Keywords:

• Longitudinal functional data analysis
• uniform convergence
• likelihood ratio test
• fractional anisotropy
• multiple sclerosis

Disclosure statement

The authors confirm that there are no relevant financial or non-financial competing interests to report.

Data availability statement

The full data that support the findings of this study cannot be made available to public due to legal restrictions. However, a part of the full data is available publicly in the R package refund (Goldsmith et al. 2020).

Additional information

Funding

The authors would like to acknowledge the support of NSF, grant number DMS 1454942.