gauss_lscv: Least squares cross-validation for Gaussian kernel density...
In mig: Multivariate Inverse Gaussian Distribution
gauss_lscv R Documentation
Least squares cross-validation for Gaussian kernel density estimation
Description
Least squares cross-validation for weighted Gaussian samples.
Usage
gauss_lscv(x, Sigma, logweights, xsamp, dxsamp, mckern = TRUE)
Arguments
x
n by d matrix of observations
Sigma
smoothing positive-definite matrix
logweights
log vector of weights
xsamp
n by d random sample for Monte Carlo estimation of bias
dxsamp
n vector of density for the points from xsamp
mckern
logical; if TRUE, uses the kernel as sampler for Monte Carlo estimation
Value
least square criterion value
mig documentation built on April 11, 2025, 5:45 p.m.
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| gauss_lscv | R Documentation |
Least squares cross-validation for Gaussian kernel density estimation
Description
Least squares cross-validation for weighted Gaussian samples.
Usage
gauss_lscv(x, Sigma, logweights, xsamp, dxsamp, mckern = TRUE)
Arguments
x |
|
Sigma |
smoothing positive-definite matrix |
logweights |
log vector of weights |
xsamp |
|
dxsamp |
|
mckern |
logical; if |
Value
least square criterion value
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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