Returns the estimated asymptotic standard deviation for the Z estimator of Kullback-Leibler's divergence. Note that this is also the asymptotic standard deviation of the plug-in estimator. See Zhang and Grabchak (2014b) for details.
Usage
KL.sd(x, y)
Arguments
x
Vector of counts from the first distribution. Must be integer valued. Each entry represents the number of observations of a distinct letter.
y
Vector of counts from the second distribution. Must be integer valued. Each entry represents the number of observations of a distinct letter.
Author
Lijuan Cao and Michael Grabchak
References
Z. Zhang and M. Grabchak (2014b). Nonparametric Estimation of Kullback-Leibler Divergence. Neural Computation, 26(11): 2570-2593.
x = c(1,3,7,4,8) # first vector of counts y = c(2,5,1,3,6) # second vector of counts KL.sd(x,y) # Estimated standard deviation KL.sd(y,x) # Estimated standard deviation