Optical Networking White Papers
Very Fast Optimal Bandwidth Selection for Univariate Kernel Density Estimation
Overview Most automatic bandwidth selection procedures for kernel density estimates require estimation of quantities involving the density derivatives. Estimation of modes and inflexion points of densities also require derivative estimates. The computational complexity of evaluating the density derivative at M evaluation points given N sample points from the density is O(MN). This paper proposes a computationally efficient å - exact approximation algorithm for the univariate Gaussian kernel based density derivative estimation that reduces the computational complexity from O(MN) to linear O(N + M). The constant depends on the desired arbitrary accuracy, å. The paper applies the density derivative evaluation procedure to estimate the optimal bandwidth for kernel density estimation, a process that is often intractable for large data sets.
| Publisher | University of Maryland | File Format | |
|---|---|---|---|
| Date Published | December 2005 | ||
| Format | White Papers | ||
| Topics | |||
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