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An R-Based Landscape Validation of a Competing Risk Model
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K-fold Crossvalidation in Canonical Analysis.

K H Liang, D J Krus, J M Webb

    Multivariate Behavioral Research
    |January 21, 2016
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    Summary
    This summary is machine-generated.

    A novel K-fold cross-validation technique enhances canonical correlation analysis by reducing sample-specific variance contamination. This computer-assisted method improves the reliability of canonical variates and correlations.

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    Area of Science:

    • Statistics
    • Machine Learning
    • Data Analysis

    Background:

    • Canonical correlation analysis (CCA) is susceptible to sample-specific variance.
    • Cross-validation is a common technique to assess model generalizability.

    Purpose of the Study:

    • To introduce and evaluate a computer-assisted K-fold cross-validation technique for CCA.
    • To assess the technique's effectiveness in mitigating sample-specific variance contamination in CCA.

    Main Methods:

    • Application of K-fold cross-validation within the framework of CCA.
    • Utilized randomly generated data sets for analysis.
    • Employed a multi-crossvalidation approach.

    Main Results:

    • The K-fold cross-validation technique effectively reduced contamination of canonical variates.
    • Canonical correlations were also found to be less contaminated by sample-specific variance.
    • The multi-crossvalidation approach demonstrated robustness.

    Conclusions:

    • Computer-assisted K-fold cross-validation is a valuable method for enhancing CCA.
    • This technique improves the reliability and generalizability of CCA results.
    • It offers a robust solution for addressing variance contamination in statistical analyses.