Non-stationary modeling of tail dependence of two subjects' concentration

Détails

ID Serval
serval:BIB_11C8E82C6CC1
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Non-stationary modeling of tail dependence of two subjects' concentration
Périodique
Annals of Applied Statistics
Auteur⸱e⸱s
Sharma K., Chavez-Demoulin V.
Statut éditorial
Publié
Date de publication
2018
Peer-reviewed
Oui
Volume
12
Numéro
2
Pages
1293-1311
Langue
anglais
Résumé
We analyse eye-tracking data to understand how people collaborate. Our dataset consists of time series of measurements for eye movements, such as spatial entropy, calculated for each subject during an experiment when several pairs of participants collaborate to accomplish a task. We observe that pairs with high collaboration quality obtain their highest values of concentration (or equivalently lowest values of spatial entropy) occurring simultaneously. In this paper, we propose a flexible model that describes the tail dependence structure between two subjects’ entropy when the pair is collaborating. More generally, we develop a generalized additive model (GAM) framework for tail dependence coefficients in the presence of covariates. As for any GAM-type model, the methodology can be used to predict collaboration quality or to explore how joint concentration depends on other cognitive operations and varies over time.
Création de la notice
11/10/2017 10:37
Dernière modification de la notice
21/08/2019 5:15
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