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Kernel-Based Hypothesis Testing

Author

Listed:
  • Wonkyung J

    (Department of Statistics, The University of North Carolina at Chapel hill, USA)

  • Ilmun K

    (Department of Statistics & Data Science, Carnegie Mellon University, USA)

Abstract

With the advent of big and complex data, there have been recent developments in statistics and related fields that overcome limitations of classical parametric inference. For instance, Gretton et al. [1] introduce kernel maximum mean discrepancy (MMD) and propose two sample testing procedures based on MMD. Unlike the classical t-test, which is only sensitive to mean differences, MMD test can detect an arbitrary difference between two multivariate distributions without imposing parametric assumptions.

Suggested Citation

  • Wonkyung J & Ilmun K, 2018. "Kernel-Based Hypothesis Testing," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(4), pages 108-110, February.
    • Handle: RePEc:adp:jbboaj:v:5:y:2018:i:4:p:108-110
    DOI: 10.19080/BBOAJ.2018.05.555668
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