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Toward Power Analysis for Partial Least Squares-Based Methods.

Angela Andreella1, Livio Finos2, Bruno Scarpa2

  • 1Department of Economics and Management, University of Trento, Trento, Italy.

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Summary
This summary is machine-generated.

This study introduces a new power analysis framework for partial least squares (PLS) methods, crucial for ensuring research replicability. The method explicitly considers complex data structures for accurate sample size estimation in applied sciences.

Keywords:
classificationomics datapartial least squarespermutation testspower analysis

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Area of Science:

  • Applied Sciences
  • Statistical Methods
  • Research Methodology

Background:

  • Power analysis is increasingly vital in applied sciences due to the replicability crisis.
  • Traditional power analysis methods are challenging for distribution-free techniques like partial least squares (PLS).

Purpose of the Study:

  • To introduce a novel methodological framework for power analysis specifically designed for PLS-based methods.
  • To address the challenges in power analysis for complex correlation structures inherent in PLS approaches.

Main Methods:

  • Utilizing Monte Carlo simulations to generate data under a false null hypothesis.
  • Leveraging latent structures estimated by PLS from pilot data.
  • Explicitly incorporating complex correlation structures into power analysis and sample size estimation.

Main Results:

  • The proposed framework effectively integrates complex data structures into power analysis.
  • Comparison of accuracy-based tests versus PLS-continuous parameter tests for power analysis.

Conclusions:

  • The new procedure provides a robust method for power analysis in PLS applications.
  • Demonstrates practical application through simulated and real data analysis, aiding in sample size determination and enhancing research reliability.