An Interval Estimation of Pearson’s Correlation Coefficient by Bootstrap Methods

Bumrungsak Phuenaree, Sirikun Sanorsap

Abstract


In this paper, we compare three confidence intervals for Pearson’s correlation coefficient which are Fisher’s transformation, standard bootstrap and percentile bootstrap methods. The performance of these confidence intervals is considered by the coverage probability and the average width. Monte Carlo simulation results for generating non-normal distribution show that the percentile bootstrap confidence interval is the best method, when the distribution is a uniform distribution and the sample sizes are larger than or equal to 50. For the logistic and Laplace distributions, the percentile bootstrap method is the most efficiency method when the sample sizes are larger than or equal to 200 and the correlation coefficients are at least 0.5. However, the Fisher method gives the best confidence interval when the correlation coefficients are 0.2.


Keywords


Confidence Interval, Pearson’s correlation, Bootstrap method, Fisher’s transformation.

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References


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DOI: https://doi.org/10.24203/ajas.v5i3.4870

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