Nonparametric inference for functional-on-scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament
Keywords
Statistical learning
Code:
30/2016
Title:
Nonparametric inference for functional-on-scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament
Date:
Friday 9th September 2016
Author(s):
Abramowicz, K.; Häger, C.; Pini, A.; Schelin, L.; Sjöstedt de Luna, S.; Vantini, S.
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Abstract:
Motivated by the analysis of the dependence of knee movement patterns during functional tasks on subject-speci c covariates, we introduce a distribution-free procedure for testing a functional-on-scalar linear model with fixed e ects. The procedure does not only test the global hypothesis on all the domain, but also selects the intervals where statistically signi ficant e ects 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 di erent treatments, and one group of healthy controls), taking individual-speci c covariates into account.