A0631
Title: Nonlinear mixed-effects scalar-on-function models and variable selection for kinematic upper limb movement data
Authors: Yafeng Cheng - MRC Biostatistics Unit (United Kingdom) [presenting]
Jian Qing Shi - Southern Univesity of Science and Technology (China)
Janet Eyre - Newcastle University (United Kingdom)
Abstract: Motivated by a collaborative research about modelling clinical assessments of upper limb function after stroke using 3D kinematic data, we present a new nonlinear mixed-effects scalar-on-function regression model with a Gaussian process (GP) prior focusing on variable selection from large number of candidates including both scalar and function variables. A novel variable selection algorithm has been developed, namely functional least angle regression (fLARS). As they are essential for this algorithm, we studied the representation of functional variables with different methods and the correlation between a scalar and a group of mixed scalar and functional variables. When the algorithm was applied to the analysis of the 3D kinetic movement data the use of the nonlinear random-effect model and the function variables significantly improved the prediction accuracy for the clinical assessment.
