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Quasi-regression with shrinkage

Author

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  • Jiang, Tao
  • Owen, Art B.

Abstract

Quasi-regression is a method of Monte Carlo approximation useful for global sensitivity analysis. This paper presents a new version, incorporating shrinkage parameters of the type used in wavelet approximation. As an example application, a black box function from machine learning is analyzed. That function is nearly a sum of functions of one and two variables and the first variable acting alone accounts for more than half of the variance.

Suggested Citation

  • Jiang, Tao & Owen, Art B., 2003. "Quasi-regression with shrinkage," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 62(3), pages 231-241.
    • Handle: RePEc:eee:matcom:v:62:y:2003:i:3:p:231-241
    DOI: 10.1016/S0378-4754(02)00253-7
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    Citations

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    Cited by:

    1. Masayoshi Mase & Art B. Owen & Benjamin Seiler, 2019. "Explaining black box decisions by Shapley cohort refinement," Papers 1911.00467, arXiv.org, revised Oct 2020.
    2. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2021. "Cohort Shapley value for algorithmic fairness," Papers 2105.07168, arXiv.org.
    3. Yang, Guijun & Wang, Zhigang & Deng, Wei, 2010. "Unbiased generalized quasi-regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 779-789, March.
    4. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2022. "Variable importance without impossible data," Papers 2205.15750, arXiv.org, revised Apr 2023.

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