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Objective Bayesian testing for the correlation coefficient under divergence-based priors

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  • Bo Peng
  • Min Wang

Abstract

The correlation coefficient is a commonly used criterion to measure the strength of a linear relationship between the two quantitative variables. For a bivariate normal distribution, numerous procedures have been proposed for testing a precise null hypothesis of the correlation coefficient, whereas the construction of flexible procedures for testing a set of (multiple) precise and/or interval hypotheses has received less attention. This paper fills the gap by proposing an objective Bayesian testing procedure using the divergence-based priors. The proposed Bayes factors can be used for testing any combination of precise and interval hypotheses and also allow a researcher to quantify evidence in the data in favor of the null or any other hypothesis under consideration. An extensive simulation study is conducted to compare the performances between the proposed Bayesian methods and some existing ones in the literature. Finally, a real-data example is provided for illustrative purposes.

Suggested Citation

  • Bo Peng & Min Wang, 2021. "Objective Bayesian testing for the correlation coefficient under divergence-based priors," The American Statistician, Taylor & Francis Journals, vol. 75(1), pages 41-51, January.
    • Handle: RePEc:taf:amstat:v:75:y:2021:i:1:p:41-51
    DOI: 10.1080/00031305.2019.1677266
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