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Nonlinear mixed-effects scalar-on-function models and variable selection

Lookup NU author(s): Dr Jian Shi, Professor Janet Eyre

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 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.


Publication metadata

Author(s): Cheng Y, Shi JQ, Eyre J

Publication type: Article

Publication status: Published

Journal: Statistics and Computing

Year: 2019

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

URL: https://doi.org/10.1007/s11222-019-09871-3

DOI: 10.1007/s11222-019-09871-3


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