Repeated measures analysis for functional data

Most of the traditional statistical methods are being adapted to the Functional Data Analysis (FDA) context. The repeated measures analysis which deals with the k-sample problem when the data are from the same subjects is investigated. Both the parametric and the nonparametric approaches are considered. Asymptotic, permutation and bootstrap approximations for the statistic distribution are developed. In order to explore the statistical power of the proposed methods in different scenarios, a Monte Carlo simulation study is carried out. The results suggest that the studied methodology can detect small differences between curves even with small sample sizes.

► The repeated measured methodology is generalized to Functional Data Analysis context. ► Both parametric and non-parametric techniques are considered. ► Bootstrap, permutation and asymptotic approximations are explored. ► In order to study the statistical power, a Monte Carlo simulation studied is developed.