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

编号 030024805

推送时间 20200720

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

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

来源期刊 STATISTICS AND COMPUTING

期 第248期

发表时间 20190409

关键词 Canonical correlation;  Functional least angle regression (fLARS);  Gaussian process prior;  Movement data;  Scalar-on-function regression;  Variable selection; 

摘要 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.

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