title: Boundary effect correction in k-nearest-neighbor estimation.
abstract: The problem of the boundary effect for the k-nearest-neighbor (kNN) estimation is addressed, and a correction method is suggested. The correction is proposed for bounded distributions, but it can be used for any set of bounded samples. We apply the proposed correction to entropy estimation of multidimensional distributions and time series, and this correction reduces considerably the bias and statistical errors in the estimation. For a small sample size or high-dimensional data, the corrected estimator outperforms the uncorrected estimator significantly. This advantage makes the kNN method applicable to more real-life situations, e.g., the analysis of biological and molecular data.
authors: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada. alizad@ece.ualberta.ca
authors: A R
authors: AR
authors: Alizad Rahvar
authors: M
authors: M
authors: Ardakani