title: Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling.
abstract: This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two different problems. First, we study the impact of age, gender and environment on the runners' performance. We derive a fair grading method that allows direct comparison of runners regardless of their age and gender. Unlike current grading systems, our approach is based not only on top world records, but on the performances of all runners. The presented methodology for comparison of densities can be adopted in many other applications straightforwardly, providing an interesting perspective to build dependent Dirichlet processes. Second, we analyze the running patterns of the marathoners in time, obtaining information that can be valuable for training purposes. We also show that these running patterns can be used to predict finishing time given intermediate interval measurements. We apply our models to New York City, Boston and London marathons.
authors.affiliation: Signal Theory and Communications Department, University Carlos III in Madrid, Madrid, Spain.|Gregorio Marañón Health Research Institute, Madrid, Spain.
authors.forename: Melanie
authors.initials: M
authors.lastname: F Pradier
authors.affiliation: Signal Theory and Communications Department, University Carlos III in Madrid, Madrid, Spain.|Gregorio Marañón Health Research Institute, Madrid, Spain.|Data Science Institute, Columbia University, New York, United States of America.
authors.forename: Francisco
authors.initials: F
authors.lastname: J R Ruiz
authors.affiliation: Signal Theory and Communications Department, University Carlos III in Madrid, Madrid, Spain.|Gregorio Marañón Health Research Institute, Madrid, Spain.|Member of the Technical Staff at Bell Labs, Alcatel-Lucent, Murray Hill, New Jersey, United States of America.
authors.forename: Fernando
authors.initials: F
authors.lastname: Perez-Cruz
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000465:Algorithms; D054874:Athletic Performance; D001499:Bayes Theorem; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012420:Running; D017678:Sex Distribution; D018709:Statistics, Nonparametric; D055815:Young Adult
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