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    Partial Least Squares (PLS) methods can be enhanced by considering component-based approaches. Researchers suggest improving PLS or using alternative methods like PLSe2 for better data analysis outcomes.

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    Area of Science:

    • Statistics
    • Data Analysis
    • Methodology Development

    Background:

    • Partial Least Squares (PLS) is a statistical method with known limitations.
    • Component-type methods have historical challenges in data analysis.
    • Previous research suggests modifying or replacing existing PLS methods.

    Purpose of the Study:

    • To explore the implications of transforming Partial Least Squares (PLS) into a component-based methodology.
    • To review historical issues associated with component-type methods.
    • To identify and suggest improved methodologies for PLS.

    Main Methods:

    • Historical analysis of component-type methods.
    • Conceptual development of implications from Rigdon's (2012) suggestion.
    • Evaluation of alternative statistical models and methods.

    Main Results:

    • Rigdon's (2012) suggestion to 'kill' PLS and create a component-based method has significant implications.
    • Historical problems with component-type methods are documented.
    • Maintaining and improving PLS is viable, alongside using alternative methods when appropriate.

    Conclusions:

    • It is beneficial to improve Partial Least Squares (PLS) while also embracing alternative methods.
    • Huang's (2013) PLSe2 methodology is a promising candidate for enhancing PLS.
    • The choice of method should depend on the specific data analytic situation.