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Bayesian random projection-based signal detection for Gaussian scale space random fields

Author

Listed:
  • Yasser Al Zaim

    (Shahid Beheshti University)

  • Mohammad Reza Faridrohani

    (Shahid Beheshti University)

Abstract

In the present paper, we are concerned with introducing a simple method for signal detection problem in one realization of a two-dimensional random field based on the one-dimensional random projection technique. Formally, we provide a Bayesian projection-based approach for signal detection in the two-dimensional Gaussian scale space random field, though it is applicable for higher dimensions. It will be shown by a series of simulation studies that our purposed method, controls the error rate in nominal level and has the high performance for signal detection, and this procedure completely distinguishes between the two hypotheses of “no signal” and the alternative. Also, we provide two applications of the proposed procedure, one from a real dataset of a two-dimensional random field of R-fMRI data of an autistic individual and the other with a two-dimensional random field of fMRI data.

Suggested Citation

  • Yasser Al Zaim & Mohammad Reza Faridrohani, 2021. "Bayesian random projection-based signal detection for Gaussian scale space random fields," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 503-532, September.
    • Handle: RePEc:spr:alstar:v:105:y:2021:i:3:d:10.1007_s10182-021-00408-6
    DOI: 10.1007/s10182-021-00408-6
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    References listed on IDEAS

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    1. Escanciano, J. Carlos, 2006. "A Consistent Diagnostic Test For Regression Models Using Projections," Econometric Theory, Cambridge University Press, vol. 22(6), pages 1030-1051, December.
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    3. Ricardo Fraiman & Leonardo Moreno & Sebastian Vallejo, 2017. "Some hypothesis tests based on random projection," Computational Statistics, Springer, vol. 32(3), pages 1165-1189, September.
    4. Shanshan Li & Ani Eloyan & Suresh Joel & Stewart Mostofsky & James Pekar & Susan Spear Bassett & Brian Caffo, 2012. "Analysis of Group ICA-Based Connectivity Measures from fMRI: Application to Alzheimer's Disease," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-9, November.
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