Non-parametric confidence intervals for covariance and correlation

Christopher Withers and Saralees Nadarajah ()

METRON, 2014, vol. 72, issue 3, 283-306

Abstract: Consider a sample of independent and identical bivariate observations. Simple consistent confidence intervals for the variances, covariance, and correlation of the underlying population are obtained from their influence functions. They contrast with their confidence intervals obtained under the assumption of normality, which are shown to be not consistent if the assumption of normality is false. Even when the marginals are normal, we show that Fisher’s $$z$$ z -transformation may be quite inappropriate. Copyright Sapienza Università di Roma 2014

Keywords: Confidence interval; Correlation; Covariance; Influence function (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s40300-013-0033-9

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