mddm {EDMeasure} R Documentation

## Martingale Difference Divergence Matrix

### Description

mddm extends martingale difference divergence from a scalar to a matrix. It encodes the linear combinations of all univariate components in Y that are conditionally mean independent of X. Only the double-centering approach is applied.

### Usage

mddm(X, Y, compute = "C")


### 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. compute The method for computation, including C: computation implemented in C code; R: computation implemented in R code.

### Value

mddm returns the martingale difference divergence matrix of Y given X.

### References

Lee, C. E., and Shao, X. (2017). Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Stationary Multivariate Time Series. Journal of the American Statistical Association, 1-14. http://dx.doi.org/10.1080/01621459.2016.1240083.

### Examples

# X, Y are vectors with 10 samples and 1 variable
X <- rnorm(10)
Y <- rnorm(10)

mddm(X, Y, compute = "C")
mddm(X, Y, compute = "R")

# X, Y are 10 x 2 matrices with 10 samples and 2 variables
X <- matrix(rnorm(10 * 2), 10, 2)
Y <- matrix(rnorm(10 * 2), 10, 2)

mddm(X, Y, compute = "C")
mddm(X, Y, compute = "R")


[Package EDMeasure version 1.2.0 Index]