mdc measures conditional mean dependence of Y given X, where each contains one variable (univariate) or more variables (multivariate).
mdc(X, Y, center ="U")
Arguments
X: A vector, matrix or data frame, where rows represent samples, and columns represent variables.
Y: A vector, matrix or data frame, where rows represent samples, and columns represent variables.
center: The approach for centering, including
U: U-centering which leads to an unbiased estimator;
D: double-centering which leads to a biased estimator.
Returns
mdc returns the squared martingale difference correlation of Y given X.
Examples
# X, Y are 10 x 2 matrices with 10 samples and 2 variablesX <- matrix(rnorm(10*2),10,2)Y <- matrix(rnorm(10*2),10,2)mdc(X, Y, center ="U")mdc(X, Y, center ="D")
References
Shao, X., and Zhang, J. (2014). Martingale difference correlation and its use in high-dimensional variable screening. Journal of the American Statistical Association, 109(507), 1302-1318. http://dx.doi.org/10.1080/01621459.2014.887012.
Park, T., Shao, X., and Yao, S. (2015). Partial martingale difference correlation. Electronic Journal of Statistics, 9(1), 1492-1517. http://dx.doi.org/10.1214/15-EJS1047.