KL.sd {EntropyEstimation}

KL.sd

Description

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(s)

Lijuan Cao and Michael Grabchak

References

Z. Zhang and M. Grabchak (2014b). Nonparametric Estimation of Kullback-Leibler Divergence. Neural Computation, 26(11): 2570-2593.

Examples

 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