Basis expansion approaches for functional analysis of variance with repeated measures

Abstract

The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves representing joint measures such as flexion angles, velocity, acceleration, and so on. In many cases the aim consists of detecting differences in gait patterns when several independent samples of subjects walk or run under different conditions (repeated measures). Classic kinematic studies often analyse discrete summaries of the sample curves discarding important information and providing biased results. As the sample data are obviously curves, a Functional Data Analysis approach is proposed to solve the problem of testing the equality of the mean curves of a functional variable observed on several independent groups under different treatments or time periods. A novel approach for Functional Analysis of Variance (FANOVA) for repeated measures that takes into account the complete curves is introduced. By assuming a basis expansion for each sample curve, two-way FANOVA problem is reduced to Multivariate ANOVA for the multivariate response of basis coefficients. Then, two different approaches for MANOVA with repeated measures are considered. Besides, an extensive simulation study is developed to check their performance. Finally, two applications with gait data are developed.

References in corpus (4)

• Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic
• Homogeneity problem for basis expansion of functional data with applications to resistive memories
• Multi-class classification of biomechanical data: A functional LDA approach based on multi-class penalized functional PLS
• Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal