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Default Priors Based on Pseudo-Likelihoods for the Poisson-GPD Model Read chapter Read first chapter

2013 | OriginalPaper | Chapter

18. How Far Can Man Go?

Authors : Isabel Fraga Alves, Laurens de Haan, Cláudia Neves

Published in: Advances in Theoretical and Applied Statistics

Publisher: Springer Berlin Heidelberg

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Abstract

In this chapter we address the question of “What is the Largest Jump at Man’s reach, given today’s state of the art?” To answer that question it will be used the best from the best, i.e., the data will be collected from the best “jumpers” from World Athletics Competitions—“Long Jump Men Outdoors” event. Our approach to the problem is based on the probability theory of extreme values (EVT) and the corresponding statistical techniques. We shall only use the top performances of World top lists. Our estimated ultimate record, i.e., the right endpoint of the jumping event, tells us what is possible to infer about the possible personal best mark, given today’s knowledge, sports conditions and rules.

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previous chapter Closed Skew Normal Stochastic Frontier Models for Panel Data
next chapter Joint Modeling of Longitudinal and Time-to-Event Data: Challenges and Future Directions
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11.
Metadata
Title
How Far Can Man Go?
Authors
Isabel Fraga Alves
Laurens de Haan
Cláudia Neves
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Advances in Theoretical and Applied Statistics

Print ISBN: 978-3-642-35587-5

Electronic ISBN: 978-3-642-35588-2

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-35588-2_18

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