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

Authors

  • Bumrungsak Phuenaree
  • Sirikun Sanorsap

Keywords:

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

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.

References

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Fisher R.A., Statistical methods for research workers, Oliver and Boyd, London, 1934

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Shang Y., “Geometric Assortative Growth Model for Small-World Networksâ€, The Scientific World Journal, Article ID759391, vol. 2014, 8 pages, 2014.

Weaver B., and Koopman R., “An SPSS Macro to Compute Confidence Intervals for Pearson’s Correlationâ€, The Quantitative Methods for Psychology, vol. 10, no. 1, pp.29-39, 2014

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Published

2017-06-30

How to Cite

Phuenaree, B., & Sanorsap, S. (2017). An Interval Estimation of Pearson’s Correlation Coefficient by Bootstrap Methods. Asian Journal of Applied Sciences, 5(3). Retrieved from https://ajouronline.com/index.php/AJAS/article/view/4870

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Section

Articles