estimate_bandwidth: Silverman bandwidth estimator for hypervolumes.
Description
Estimates bandwidth vector from data using a Silverman bandwidth estimator applied independently to each axis of the data. This approach provides a heuristic when no other methods are available to choose kernel bandwidth.
Usage
estimate_bandwidth(data)
Arguments
data
m x n matrix or data frame, where m is the number of observations and n the number of dimensions.
Value
Vector of length n with each entry corresponding to the estimated bandwidth along each axis.
Details
The Silverman estimator is defined as 1.06 * sd(X) * m^(-1/5) where m is the number of observations and X is the data vector in each dimension. Note that this estimator is optimal only for univariate normal data and not for the box kernels implemented by the hypervolume algorithms.