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Bayesian Modeling of Footrace Finishing Times

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  • Shotwell Matthew S

    (Medical University of South Carolina)

  • Slate Elizabeth H

    (Medical University of South Carolina)

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    Abstract

    The 2007 Cooper River Bridge Run (CRBR) dataset has over 28,000 observations. The histogram of finishing times has a bimodal shape and varies in location and scale with the participants' gender and age. Four different mixture models are developed and assessed in terms of their ability to describe the features of this dataset. We discuss the "label switching" problem in the present context, review current solutions, and how they may be inadequate when covariates are present. The Bayes factor and the more modern deviance information criterion (DIC) are compared as devices for model selection. MCMC output is presented with plots of the model fits. We conclude with remarks on unanswered questions with regard to Bayesian mixture modeling, and the potential impact of this analysis for future Cooper River Bridge Runs. Raw data and BUGS programs corresponding to each model are available.

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    Bibliographic Info

    Article provided by De Gruyter in its journal Journal of Quantitative Analysis in Sports.

    Volume (Year): 6 (2010)
    Issue (Month): 3 (July)
    Pages: 1-21

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    Handle: RePEc:bpj:jqsprt:v:6:y:2010:i:3:n:5

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