lnn_entropy function

Local Nearest Neighbor (LNN) Entropy Estimator

Local Nearest Neighbor entropy estimator using Gaussian kernel and kNN selected bandwidth. Entropy is estimated by taking a Monte Carlo estimate using local kernel density estimate of the negative-log density.

lnn_entropy(data, k = 5, tr = 30, bw = NULL)

Arguments

• data: Matrix of sample observations, each row is an observation.
• k: Order of the local kNN bandwidth selection.
• tr: Order of truncation (number of neighbors to include in entropy).
• bw: Bandwidth (optional) manually fix bandwidth instead of using local kNN bandwidth selection.

References

Loader, C. (1999). Local regression and likelihood. Springer Science & Business Media.

Gao, W., Oh, S., & Viswanath, P. (2017). Density functional estimators with k-nearest neighbor bandwidths. IEEE International Symposium on Information Theory - Proceedings, 1, 1351–1355.

Examples

set.seed(123)
x <- rnorm(1000)
print(lnn_entropy(x))
#analytic entropy
print(0.5*log(2*pi*exp(1)))