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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Steve Rigdon, PhD

4 Issues per year


CiteScore 2017: 0.67

SCImago Journal Rank (SJR) 2017: 0.290
Source Normalized Impact per Paper (SNIP) 2017: 0.853

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2194-6388
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Volume 9, Issue 1

Issues

Volume 1 (2005)

Is high-altitude mountaineering Russian roulette?

Edward K. Cheng
  • Corresponding author
  • Vanderbilt University Law School, Nashville, TN 37203, USA; and Department of Statistics, Columbia University, New York, NY 10027, USA
  • Thanks to Jim Albert, Andreas Buja, Andrew Gelman, Ray Huey, David Madigan, Zach Shahn, and two anonymous reviewers for helpful comments and conversations. Research was conducted under Columbia University IRB protocol IRB-AAAF3302 (exempt) and supported by a Bedayn Research Grant from the American Alpine Club.
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Published Online: 2013-03-30 | DOI: https://doi.org/10.1515/jqas-2012-0038

Abstract

Whether the nature of the risks associated with climbing high-altitude (8000 m) peaks is in some sense “controllable” is a longstanding debate in the mountaineering community. Well-known mountaineers David Roberts and Ed Viesturs explore this issue in their recent memoirs. Roberts views the primary risks as “objective” or uncontrollable, whereas Viesturs maintains that experience and attention to safety can make a significant difference. This study sheds light on the Roberts-Viesturs debate using a comprehensive dataset of climbing on Nepalese Himalayan peaks. To test whether the data is consistent with a constant failure rate model (Roberts) or a decreasing failure rate model (Viesturs), it draws on Total Time on Test (TTT) plots from the reliability engineering literature and applies graphical inference techniques to them.

Keywords: 8000 m; mountaineering; survival analysis; total time on test (TTT); graphical inference

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About the article

Corresponding author: Edward K. Cheng, Vanderbilt University Law School, Nashville, TN 37203, USA; and Department of Statistics, Columbia University, New York, NY 10027, USA


Published Online: 2013-03-30


This 60% figure would appear to come from

.

Further extensions of the method might involve outsourcing the visual processing to external observers, such as those provided by a service like Amazon Mechanical Turk (mTurk) (Buja et al. 2009: 4381). Amazon mTurk is particularly useful for testing at p-values lower than 0.05, since there are limits to the number of graphs a person can simultaneously compare, and space (and boredom) limitations preclude including pages of plots in journals.

TTT plots for ascent rates would necessarily require different calculations than those proposed in this Article, because climbers can have multiple ascents. Unlike death, success does not censor further observation of a climber.


Citation Information: Journal of Quantitative Analysis in Sports, Volume 9, Issue 1, Pages 1–14, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2012-0038.

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