Comparison of Parametric and Non-Parametric Population Modelling of Sport Performances

被引：0

作者：

Bermon, Stephane ^{[1
]}

Metelkina, Asya ^{[2
]}

Rendas, Maria Joao ^{[2
]}

机构：

[1] Int Ass Athlet Federat, Hlth & Sci Dept, Monaco, Monaco

[2] CNRS, Lab I3S, Sophia Antipolis, France

来源：

2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2018年

关键词：

Functional data; longitudinal population models; mixed-effects models; Gaussian Processes; Hierarchical Bayesian Gauss-Wishart models; athletic performance; FUNCTIONAL DATA;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This work compares the performance of parametric mixed-effects models to a completely non-parametric (NP) approach for modelling life-long evolution of competition performance for athletes. The difficulty of the problem lies in the strongly unbalanced characteristics of the functional dataset. The prediction performance of the identified models is compared, revealing the advantages and limitations of the two approaches. As far as we know this is the first time NP modelling of athletic performance is attempted, our study confirming its appropriateness whenever sufficiently rich datasets are available.

引用

页码：301 / 305

页数：5

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