Unbiased distance covariance statistics
This function computes unbiased estimators of squared distance covariance, distance variance, and a bias-corrected estimator of (squared) distance correlation.
dcovU_stats(Dx, Dy)
Dx |
distance matrix of first sample |
Dy |
distance matrix of second sample |
The unbiased (squared) dcov is inner product definition of dCov, in the Hilbert space of U-centered distance matrices.
The sample sizes (number of rows) of the two samples must agree, and samples must not contain missing values. The arguments must be square symmetric matrices.
dcovU_stats returns a vector of the components of bias-corrected
dcor: [dCovU, bcdcor, dVarXU, dVarYU].
Unbiased distance covariance (SR2014) corresponds to the biased
(original) dCov^2. Since dcovU is an
unbiased statistic, it is signed and we do not take the square root.
For the original distance covariance test of independence (SRB2007,
SR2009), the distance covariance test statistic is the V-statistic
n V_n^2 (not dCov).
Similarly, bcdcor is bias-corrected, so we do not take the
square root as with dCor.
Maria L. Rizzo mrizzo@bgsu.edu and Gabor J. Szekely
Szekely, G.J. and Rizzo, M.L. (2014), Partial Distance Correlation with Methods for Dissimilarities. Annals of Statistics, Vol. 42 No. 6, 2382-2412.
Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007),
Measuring and Testing Dependence by Correlation of Distances,
Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794.
doi: 10.1214/009053607000000505
Szekely, G.J. and Rizzo, M.L. (2009),
Brownian Distance Covariance,
Annals of Applied Statistics,
Vol. 3, No. 4, 1236-1265.
doi: 10.1214/09-AOAS312
x <- iris[1:50, 1:4] y <- iris[51:100, 1:4] Dx <- as.matrix(dist(x)) Dy <- as.matrix(dist(y)) dcovU_stats(Dx, Dy)
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