The Generalized Fisher Transformation: Finite-Sample Properties and Inference

We study the finite-sample behavior of the Generalized Fisher Transformation (GFT), the parametrization of a correlation matrix C by γ(C)=operatornamevecllog C. The GFT coordinates extend Fisher's transformation to dimension n>2: for elliptical data their finite-sample distributions are close to Gaussian. More strikingly, the coordinates are nearly uncorrelated and their covariance is largely invariant to C. This approximate orthogonality and invariance make GFT-based inference far better behaved in finite samples than inference based on sample correlations or element-wise Fisher transformed correlations, yielding estimation errors that are approximately Gaussian, weakly dependent, and nearly pivotal.