School of Statistics, East China Normal University, Shanghai, People's Republic of China
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
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
jxu@stat.ecnu.edu.cn
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.
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
To link to this article: https://doi.org/10.1080/24754269.2023.2164970