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.

Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

“…In this manner, different curves with the same V set and V reset lead to a non-negligible variability. We perform this analysis, which is fully described at the mathematical level in the Supporting Information, using functional data analysis. − This study can be considered two-dimensional because each element is a complete I – V curve instead of a single V set and V reset value. Consequently, by considering the variability generated all along the I – V curves, a holistic perspective is achieved, and a more exhaustive variability estimation can be performed.…”