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Bayesian inference for stationary points in Gaussian process regression models for event‐related potentials analysis

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  • Cheng‐Han Yu
  • Meng Li
  • Colin Noe
  • Simon Fischer‐Baum
  • Marina Vannucci

Abstract

Stationary points embedded in the derivatives are often critical for a model to be interpretable and may be considered as key features of interest in many applications. We propose a semiparametric Bayesian model to efficiently infer the locations of stationary points of a nonparametric function, which also produces an estimate of the function. We use Gaussian processes as a flexible prior for the underlying function and impose derivative constraints to control the function's shape via conditioning. We develop an inferential strategy that intentionally restricts estimation to the case of at least one stationary point, bypassing possible mis‐specifications in the number of stationary points and avoiding the varying dimension problem that often brings in computational complexity. We illustrate the proposed methods using simulations and then apply the method to the estimation of event‐related potentials derived from electroencephalography (EEG) signals. We show how the proposed method automatically identifies characteristic components and their latencies at the individual level, which avoids the excessive averaging across subjects that is routinely done in the field to obtain smooth curves. By applying this approach to EEG data collected from younger and older adults during a speech perception task, we are able to demonstrate how the time course of speech perception processes changes with age.

Suggested Citation

  • Cheng‐Han Yu & Meng Li & Colin Noe & Simon Fischer‐Baum & Marina Vannucci, 2023. "Bayesian inference for stationary points in Gaussian process regression models for event‐related potentials analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 629-641, June.
  • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:629-641
    DOI: 10.1111/biom.13621
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    References listed on IDEAS

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    3. Ghosal,Subhashis & van der Vaart,Aad, 2017. "Fundamentals of Nonparametric Bayesian Inference," Cambridge Books, Cambridge University Press, number 9780521878265, September.
    4. repec:bla:biomet:v:71:y:2015:i:4:p:1090-1100 is not listed on IDEAS
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