| Copyright | (c) 2011 Bryan O'Sullivan |
|---|---|
| License | BSD3 |
| Maintainer | bos@serpentine.com |
| Stability | experimental |
| Portability | portable |
| Safe Haskell | None |
| Language | Haskell2010 |
Statistics.Sample.KernelDensity
Contents
Description
Kernel density estimation. This module provides a fast, robust, non-parametric way to estimate the probability density function of a sample.
This estimator does not use the commonly employed "Gaussian rule of thumb". As a result, it outperforms many plug-in methods on multimodal samples with widely separated modes.
Estimation functions
Arguments
The number of mesh points to use in the uniform discretization
of the interval (min,max). If this value is not a power of
two, then it is rounded up to the next power of two.
Gaussian kernel density estimator for one-dimensional data, using the method of Botev et al.
The result is a pair of vectors, containing:
- The coordinates of each mesh point. The mesh interval is chosen
to be 20% larger than the range of the sample. (To specify the
mesh interval, use
kde_.) - Density estimates at each mesh point.
Arguments
The number of mesh points to use in the uniform discretization
of the interval (min,max). If this value is not a power of
two, then it is rounded up to the next power of two.
Lower bound (min) of the mesh range.
Upper bound (max) of the mesh range.
Gaussian kernel density estimator for one-dimensional data, using the method of Botev et al.
The result is a pair of vectors, containing:
- The coordinates of each mesh point.
- Density estimates at each mesh point.
References
Botev. Z.I., Grotowski J.F., Kroese D.P. (2010). Kernel density estimation via diffusion. Annals of Statistics 38(5):2916–2957. http://arxiv.org/pdf/1011.2602