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

R L Jensen1, G M Kline

  • 1Department of Kinesiology, Health Promotion and Recreation, University of North Texas, Denton 76203.

Medicine and Science in Sports and Exercise
|July 1, 1994
PubMed
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This paper introduces resampling techniques, powerful computer-intensive statistical methods for hypothesis testing and data description. It focuses on cross-validation, using computational power to generate pseudosamples for analysis.

Area of Science:

  • Statistics
  • Computational Science

Background:

  • Recent advances in high-speed computing have spurred the development of new statistical methods.
  • Computer-intensive methods, broadly termed resampling techniques, are increasingly utilized.

Purpose of the Study:

  • To introduce the resampling approach in cross-validation.
  • To provide a brief discussion of the motivation behind these methods.
  • To present an example within an exercise science context.

Main Methods:

  • Focuses on resampling techniques, including randomization tests, jackknife, bootstrap, and cross-validation.
  • Utilizes computer power to rapidly resample pseudosamples from existing datasets.
  • Includes random generation of pseudosamples from theoretical probability distributions (Monte Carlo method).

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Main Results:

  • Resampling techniques offer versatile applications in inferential hypothesis testing and exploratory data description.
  • The core principle involves leveraging computational power for extensive data resampling.
  • Cross-validation is highlighted as a key application of resampling.

Conclusions:

  • Resampling techniques represent a significant advancement in statistical analysis, driven by computational power.
  • These methods provide robust approaches for both hypothesis testing and data exploration.
  • The paper serves as an introductory guide to resampling in cross-validation, with practical relevance in fields like exercise science.