A finite mixture latent trajectory model for modeling ultrarunners' behavior in a 24-hour race

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Title: A finite mixture latent trajectory model for modeling ultrarunners' behavior in a 24-hour race
Authors: Bartolucci, Francesco
Murphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/6845
Date: Dec-2015
Abstract: A finite mixture latent trajectory model is developed to study the performance and strategy of runners in a 24-h long ultra running race. The model facilitates clustering of runners based on their speed and propensity to rest and thus reveals the strategies used in the race. Inference for the adopted latent trajectory model is achieved using an expectation-maximization algorithm. Fitting the model to data from the 2013 World Championships reveals three clearly separated clusters of runners who exhibit different strategies throughout the race. The strategies show that runners can be grouped in terms of their average moving speed and their propensity to rest during the race. The effect of age and gender on the probability of belonging to each cluster is also investigated.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: De Gruyter
Keywords: Machine learning;Statistics;Clustering;Expectation-maximization algorithm;Non-ignorable drop-out;Ultra running
DOI: 10.1515/jqas-2014-0060
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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