Description Usage Arguments Details Value References Examples
Computes inverse of bandwidth matrix using rule-of-thumb from Silverman (1986).
1 | H.inv.select(X, H.mult = 1)
|
X |
Matrix of inputs |
H.mult |
Scaling factor for rule-of-thumb smoothing matrix |
This method performs selection of (inverse) multivariate bandwidth matrices using Silverman's (1986) rule-of-thumb. Specifically, Silverman recommends setting the bandwidth matrix to
H_{jj}^{1/2} = ≤ft(\frac{4}{M + 2}\right)^{1 / (M + 4)}\times N^{-1 / (M + 4)}\times \mbox{sd}(x^j) \mbox{\ \ \ \ for }j=1,...,M
H_{ab} = 0\mbox{\ \ \ \ for }a\neq b
where M is the number of inputs, N is the number of observations, and \mbox{sd}(x^j) is the sample standard deviation of input j.
Returns inverse bandwidth matrix
Silvermansnfa
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