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    Area of Science:

    • Multivariate statistics
    • Statistical modeling

    Background:

    • Redundancy analysis quantifies shared variance between variable sets.
    • Previous methods by Stewart, Love, and van den Wollenberg established redundancy measures and maximization techniques.
    • Johansson extended redundancy analysis to derive optimal Y-variates given X-variates.

    Purpose of the Study:

    • To demonstrate that redundancy maximization, including Johansson's extension, can be achieved using a straightforward iterative algorithm.
    • To link redundancy maximization with Johansson's extension to Wold's Partial Least Squares (PLS) methodology.

    Main Methods:

    • The study proposes a simple iterative algorithm.
    • The algorithm is based on Wold's Partial Least Squares (PLS).
    • The method facilitates redundancy maximization as extended by Johansson.

    Main Results:

    • The proposed iterative algorithm effectively accomplishes redundancy maximization.
    • The algorithm integrates Johansson's extension for optimal Y-variate derivation.
    • The approach leverages the principles of Partial Least Squares.

    Conclusions:

    • Redundancy maximization, incorporating Johansson's extension, is efficiently achievable via a simple iterative algorithm.
    • The algorithm provides a practical computational method rooted in Partial Least Squares.
    • This approach offers a streamlined way to analyze shared variance in multivariate datasets.