Kernelwidth
Definition:
w = kernelwidth( vX ) returns the estimated optimal kernel bandwidth for a given vector vX.
/math-56a67a1cb17bb73af69637714e01c101.png "w = 0.9 \sigma n^{-\frac{1}{5}}")
where n is the size of vector vX, and
/math-e66ec91d3e7ecc7db9890d06abc413ae.png "\sigma = \min( \hat{\sigma}, \ IQR/1.349 )")
, /math-86f07e149891702b24e6c04bcb41b5e7.png "\hat{\sigma}")
is the standard deviation of vX, and IQR is the interquartile range of vX.
Parameters:
- vX (input, vector)
- Distributed samples used to estimate bandwidth
- w (output, double)
- Estimated optimal bandwidth for kernel density,
/math-1b2fff7e3c87bf42cb1237475b736af8.png "w > 0")
Reference
Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.