CWavDE: Simple wavelet density estimator with hard thresholding

In wavethresh: Wavelets Statistics and Transforms

Simple wavelet density estimator with hard thresholding

Description

This function implements the density estimator with hard thresholding described by Hall, P. and Patil, P. (1995) Formulae for mean integrated squared error of nonlinear wavelet-based density estimators, Ann. Statist., 23, 905-928.

Usage

CWavDE(x, Jmax, threshold=0, nout=100, primary.resolution=1, filter.number=10,
	family="DaubLeAsymm", verbose=0, SF=NULL, WV=NULL)

Arguments

x	Vector of real numbers. This is the data for which you want a density estimate for
Jmax	The maximum resolution of wavelets
threshold	The hard threshold value for the wavelet coefficients
nout	The number of ordinates in the density estimate
primary.resolution	The usual wavelet density estimator primary resolution
filter.number	The wavelet filter number, see filter.select
family	The wavelet family, see filter.select
verbose	The level of reporting performed by the function, legit values are 0, 1 or 2, with 2 being more reports
SF	Scaling function values in format as returned by draw.default
WV	Wavelet function values in format as returned by draw.default

Details

As the description.

Value

A list containing the following components:

x	A vector of length nout that covers the range of the input data x, plus some more depending on the support of the wavelet and the primary resolution.
y	A vector of length nout that contains the output wavelet density estimate
sfix	The integer values of the translates of the scaling functions used in the estimate
wvixmin	As for sfix, but a vector of length Jmax which contains the minimum integer wavelet translates
wvixmax	As for wvixmin, but with the maxima

Author(s)

G P Nason

Examples

#
# Let's generate a bi-modal artificial set of data.
#
x <- c( rnorm(100), rnorm(100, 10))
#
# Now perform simple wavelet density estimate
#
wde <- CWavDE(x, Jmax=10, threshold=1)
#
# Plot results
#
## Not run: plot(wde$x, wde$y, type="l")

wavethresh documentation built on Nov. 16, 2022, 5:16 p.m.