Nonparametric inference for functional-on-scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament
Friday 9th September 2016
Abramowicz, K.; Häger, C.; Pini, A.; Schelin, L.; Sjöstedt de Luna, S.; Vantini, S.
Motivated by the analysis of the dependence of knee movement patterns during functional tasks on subject-specic covariates, we introduce a distribution-free procedure for testing a functional-on-scalar linear model with fixed eects. The procedure does not only test the global hypothesis on all the domain, but also selects the intervals where statistically significant eects are detected. We prove that the proposed tests are provided with an asymptotic control of the interval-wise error rate, i.e., the probability of falsely rejecting any interval of true null hypotheses. The procedure is applied to one-leg hop data from a study on anterior cruciate ligament injury. We compare knee kinematics of three groups of individuals (two injured groups with dierent treatments, and one group of healthy controls), taking individual-specic covariates into account.