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Related Concept Videos

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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Trihybrid Crosses
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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
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A K-fold Averaging Cross-validation Procedure.

Yoonsuh Jung1, Jianhua Hu2

  • 1Department of Statistics, University of Waikato, Hamilton, New Zealand.

Journal of Nonparametric Statistics
|September 16, 2016
PubMed
Summary
This summary is machine-generated.

A new K-fold cross-validation method averages optimal models from each fold, reducing variance for more stable and efficient parameter estimation compared to classical K-fold cross-validation.

Keywords:
Cross-validationModel AveragingModel Selection

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Area of Science:

  • Statistics
  • Machine Learning

Background:

  • Cross-validation is crucial for model estimation and variable selection.
  • Classical K-fold cross-validation is a widely used technique.

Purpose of the Study:

  • To introduce a novel K-fold cross-validation procedure.
  • To enhance model stability and parameter estimation efficiency.

Main Methods:

  • A new K-fold cross-validation approach is proposed.
  • Candidate optimal models from each hold-out fold are selected and averaged.
  • The method is applied to parameter sparsity regularization and quantile smoothing splines.

Main Results:

  • The proposed method significantly reduces the variance of estimates.
  • It yields more stable and efficient parameter estimation than classical K-fold cross-validation.
  • Asymptotic equivalence to classical methods is shown in linear regression.

Conclusions:

  • The new K-fold cross-validation procedure offers improved performance.
  • It demonstrates broad applicability and promise in statistical modeling.
  • The averaging effect is key to enhanced estimation stability and efficiency.