NONSTATIONARY MODELLING OF TAIL DEPENDENCE OF TWO SUBJECTS' CONCENTRATION

作者：Sharma Kshitij^*; Chavez Demoulin Valerie; Dillenbourg Pierre

来源：Annals of Applied Statistics, 2018, 12(2): 1293-1311.

DOI：10.1214/17-AOAS1111

• Collaborative learning
• copulas
• entropy
• generalized additive models
• tail dependence

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.

• 出版日期2018-6