Lookup NU author(s): Dr Jian Shi,
Professor Janet Eyre
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2019, The Author(s). This paper is motivated by our collaborative research and the aim is to model clinical assessments of upper limb function after stroke using 3D-position and 4D-orientation movement data. We present a new nonlinear mixed-effects scalar-on-function regression model with a Gaussian process prior focusing on the variable selection from a large number of candidates including both scalar and function variables. A novel variable selection algorithm has been developed, namely functional least angle regression. As it is 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. We also propose a new stopping rule for practical use. This algorithm is efficient and accurate for both variable selection and parameter estimation even when the number of functional variables is very large and the variables are correlated. And thus the prediction provided by the algorithm is accurate. Our comprehensive simulation study showed that the method is superior to other existing variable selection methods. When the algorithm was applied to the analysis of the movement data, the use of the nonlinear random-effect model and the function variables significantly improved the prediction accuracy for the clinical assessment.
Author(s): Cheng Y, Shi JQ, Eyre J
Publication type: Article
Publication status: Published
Journal: Statistics and Computing
Pages: Epub ahead of print
Online publication date: 09/04/2019
Acceptance date: 02/04/2019
Date deposited: 30/04/2019
ISSN (print): 0960-3174
ISSN (electronic): 1573-1375
Publisher: Springer New York LLC
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