The resampling cross-validation technique in exercise science: modelling rowing power.

Abstract:

:The past 10-15 yr have witnessed a rapid increase in the development of new (and not so new) statistical methods that capitalize on recent advances in high-speed computing. These computer-intensive methods are often broadly referred to as resampling techniques and take several forms depending on the specific details of the procedure and the information of interest. Resampling techniques can be used both for inferential hypothesis testing as well as exploratory data description. Regardless of which method is employed, the central unifying theme is based upon the computer's power to rapidly resample many pseudosamples from a known (in-hand) data set (e.g., randomization tests, jackknife, boot-strap, cross-validation) or to randomly generate many pseudosamples from a theoretical probability distribution (e.g., normal, binomial, Poisson) with some known parameters (Monte Carlo method). This paper is not intended as a detailed description of computer-intensive methods, but only as an introduction to the resampling approach in cross-validation. A brief discussion of the motivation and an example in an exercise science context will be presented.

journal_name

Med Sci Sports Exerc

authors

Jensen RL,Kline GM

subject

Has Abstract

pub_date

1994-07-01 00:00:00

pages

929-33

issue

7

eissn

0195-9131

issn

1530-0315

journal_volume

26

pub_type

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