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Nonparametric inference for functional‐on‐scalar linear models applied to knee kinematic hop data after injury of the anterior cruciate ligament
Author(s) -
Abramowicz Konrad,
Häger Charlotte K.,
Pini Alessia,
Schelin Lina,
Sjöstedt de Luna Sara,
Vantini Simone
Publication year - 2018
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12333
Subject(s) - mathematics , anterior cruciate ligament , covariate , kinematics , scalar (mathematics) , statistical hypothesis testing , null hypothesis , statistical inference , inference , nonparametric statistics , statistics , confidence interval , orthodontics , physical medicine and rehabilitation , surgery , medicine , computer science , artificial intelligence , geometry , physics , classical mechanics
Motivated by the analysis of the dependence of knee movement patterns during functional tasks on subject‐specific covariates, we introduce a distribution‐free procedure for testing a functional‐on‐scalar linear model with fixed effects. The procedure does not only test the global hypothesis on the entire domain but also selects the intervals where statistically significant effects are detected. We prove that the proposed tests are provided with an asymptotic control of the intervalwise error rate, that is, 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 different treatments and one group of healthy controls), taking individual‐specific covariates into account.