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Interval estimation for the normal correlation coefficient

Author

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  • Sun, Y.
  • Wong, A.C.M.

Abstract

Inference concerning the correlation coefficient of two random variables from the bivariate normal distribution has been investigated by many authors. In particular, Fisher [1915. Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika 10, 507-521] and Hotelling [1953. New light on the correlation coefficient and its transform. J. Roy. Statist. Soc. Ser. B 15, 193-232] derived various exact forms of the density for the sample correlation coefficient. However, obtaining confidence intervals based on these densities can be computational intensive. Fisher [1921. On the "probable error" of a coefficient of correlation deduced from a small sample. Metron 1, 3-32], Hotelling [1953. New light on the correlation coefficient and its transform. J. Roy. Statist. Soc. Ser. B 15, 193-232], and Ruben [1966. Some new results on the distribution of the sample correlation coefficient. J. Roy. Statist. Soc. Ser. B 28, 513-525] suggested several simple approximations for obtaining confidence intervals for the correlation coefficient. In this paper, a likelihood-based higher-order asymptotic method is proposed to obtain confidence intervals for the correlation coefficient. The proposed method is based on the results in Fraser and Reid [1995. Ancillaries and third order significance. Utilitas Math. 7, 33-53] and Fraser et al. [1999. A simple general formula for tail probabilities for frequentist and Bayesian inference. Biometrika 86, 249-264]. Simulation results indicated that the proposed method is very accurate even when the sample size is small.

Suggested Citation

  • Sun, Y. & Wong, A.C.M., 2007. "Interval estimation for the normal correlation coefficient," Statistics & Probability Letters, Elsevier, vol. 77(17), pages 1652-1661, November.
  • Handle: RePEc:eee:stapro:v:77:y:2007:i:17:p:1652-1661
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    Cited by:

    1. Riccardo Bramante & Giovanni Petrella & Diego Zappa, 2015. "On the use of the market model R-square as a measure of stock price efficiency," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 379-391, February.

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