dist_kde: Create distributional object based on a kernel density estimate
Description
Creates a distributional object using a kernel density estimate with a
Gaussian kernel obtained from the kde() function. The bandwidth
can be specified; otherwise the kde_bandwidth() function is used.
The cdf, quantiles and moments are consistent with the kde. Generating
random values from the kde is equivalent to a smoothed bootstrap.
Usage
dist_kde(
y,
h = NULL,
H = NULL,
method = c("normal", "robust", "plugin", "lookout"),
...
)
Arguments
y
Numerical vector or matrix of data, or a list of such objects. If a
list is provided, then all objects should be of the same dimension. e.g.,
all vectors, or all matrices with the same number of columns.
h
Bandwidth for univariate distribution. Ignored if y has 2 or more
columns. If NULL, the kde_bandwidth function is used.
H
Bandwidth matrix for multivariate distribution. If NULL,
the kde_bandwidth function is used.
method
The method of bandwidth estimation to use. See kde_bandwidth()
for details. Ignored if h or H are specified.
...
Other arguments are passed to kde.
References
Rob J Hyndman (2026) "That's weird: Anomaly detection using R", Section 2.7 and 3.9,
https://OTexts.com/weird/.