Modelling dynamics of marathons – A mixture model approach

Hok Shing Kwong and Saralees Nadarajah

Physica A: Statistical Mechanics and its Applications, 2019, vol. 534, issue C

Abstract: In this paper, statistical properties of marathon dynamics are studied. We find that changes of velocity in marathons follow an unconventional mechanism; in which the log-change of velocity is highly dependent on current velocity with a complex relationship. The conditional distributions of log-change of velocity exhibit patterns of varying means, variances, and skewnesses; as such, the overall velocity distributions are also found to have departed from Gaussian. We illustrate the mechanism with a finite mixture of generalized linear regressions with varying weights and skew normal errors; and we show that the completion time distribution can be approximated by skewed distributions.

Keywords: Finite mixture model; Generalized linear model; HMC; Logistic regression; MCMC; Skew exponential power distribution; Skew normal distribution; Velocity distribution (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843711930408X
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:534:y:2019:i:c:s037843711930408x

DOI: 10.1016/j.physa.2019.04.034

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().