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The pitfalls of using Gaussian Process Regression for normative modeling

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  • Bohan Xu
  • Rayus Kuplicki
  • Sandip Sen
  • Martin P Paulus

Abstract

Normative modeling, a group of methods used to quantify an individual’s deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.

Suggested Citation

  • Bohan Xu & Rayus Kuplicki & Sandip Sen & Martin P Paulus, 2021. "The pitfalls of using Gaussian Process Regression for normative modeling," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0252108
    DOI: 10.1371/journal.pone.0252108
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    References listed on IDEAS

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    1. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    2. Anjishnu Banerjee & David B. Dunson & Surya T. Tokdar, 2013. "Efficient Gaussian process regression for large datasets," Biometrika, Biometrika Trust, vol. 100(1), pages 75-89.
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