Regression models of Pearson correlation coefficient

Abdisa G. Dufera, Tiantian Liu and Jin Xu

Statistical Theory and Related Fields, 2023, vol. 7, issue 2, 97-106

Abstract: We propose two simple regression models of Pearson correlation coefficient of two normal responses or binary responses to assess the effect of covariates of interest. Likelihood-based inference is established to estimate the regression coefficients, upon which bootstrap-based method is used to test the significance of covariates of interest. Simulation studies show the effectiveness of the method in terms of type-I error control, power performance in moderate sample size and robustness with respect to model mis-specification. We illustrate the application of the proposed method to some real data concerning health measurements.

Date: 2023
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DOI: 10.1080/24754269.2023.2164970

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