Review Articles

Regression models of Pearson correlation coefficient

Abdisa G. Dufera ,

School of Statistics, East China Normal University, Shanghai, People's Republic of China

Tiantian Liu ,

School of Statistics, East China Normal University, Shanghai, People's Republic of China; Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel

Jin Xu

School of Statistics, East China Normal University, Shanghai, People's Republic of China; Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, East China Normal University, Shanghai, People's Republic of China

Pages | Received 06 Sep. 2022, Accepted 01 Jan. 2023, Published online: 11 Jan. 2023,
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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.

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To cite this article: Abdisa G. Dufera, Tiantian Liu & Jin Xu (2023) Regression models of Pearson correlation coefficient, Statistical Theory and Related Fields, 7:2, 97-106, DOI: 10.1080/24754269.2023.2164970

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