Description Usage Arguments Details Value Author(s) References See Also Examples
Find asymptotically optimal bandwidth for Gaussian kernel density estimation by minimizing mean squared error at the first data point.
1 | bw.mse.pdf.asym(x,iter.max=1L,eps=1e-6,start.bw=bw.nrd, verbose=FALSE)
|
x |
A numeric vector whose density is to be estimated. |
iter.max |
The max number of iterations. For an iterative procedure, set this to be greater than 1L. |
eps |
A small positive number such that iterations are terminated when the difference between successive bandwidths is below |
start.bw |
A function, a character, or a positive number. If being a function or a character, the corresponding function will be called with |
verbose |
Logical, when |
The procedure starts from the starting bandwidth specified by start.bw
. The new bandwidth will be computed as
( {Rf(x_1)} / [N{f''(x_1)}^2] )^{1/5}
where R
is approximately 0.2821, N
is the sample size and f
is the density estimate using the current bandwidth.
A numeric positive scalar, giving the final bandwidth.
Long Qu
Parzen, E. (1962). On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics 33: 1065–1076.
Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York.
Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.
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