Kernel-Based Hypothesis Testing

Wonkyung J and Ilmun K
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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

Biostatistics and Biometrics Open Access Journal, 2018, vol. 5, issue 4, 108-110

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.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:5:y:2018:i:4:p:108-110

DOI: 10.19080/BBOAJ.2018.05.555668

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