awstestprop: Propagation condition for adaptive weights smoothing

In aws: Adaptive Weights Smoothing

Propagation condition for adaptive weights smoothing

Description

The function enables testing of the propagation condition in order to select appropriate values for the parameter lambda in function aws.

Usage

awstestprop(dy, hmax, theta = 1, family = "Gaussian", lkern = "Triangle",
            aws = TRUE, memory = FALSE, shape = 2, homogeneous=TRUE, varadapt=FALSE,
            ladjust = 1, spmin=0.25, seed = 1, minlevel=1e-6, maxz=25, diffz=.5,
            maxni=FALSE, verbose=FALSE)
pawstestprop(dy, hmax, theta = 1, family = "Gaussian", lkern = "Triangle",
             aws = TRUE, patchsize=1, shape = 2,
             ladjust = 1, spmin = 0.25, seed = 1, minlevel = 1e-6,
             maxz = 25, diffz = .5, maxni = FALSE, verbose = FALSE)

Arguments

dy	Dimension of grid used in 1D, 2D or 3D. May also be specified as an array of values. In this case data are generated with parameters dy-mean(dy)+theta and the propagation condition is testet as if theta is the true parameter. This can be used to study properties for a slighty misspecified structural assumption.
hmax	Maximum bandwidth.
theta	Parameter determining the distribution in case of family %in% c("Poisson","Bernoulli")
family	family specifies the probability distribution. Default is family="Gaussian", also implemented are "Bernoulli", "Poisson", "Exponential", "Volatility", "Variance" and "NCchi". family="Volatility" specifies a Gaussian distribution with expectation 0 and unknown variance. family="Volatility" specifies that p*y/theta is distributed as \chi^2 with p=shape degrees of freedom. family="NCchi" uses a noncentral Chi distribution with p=shape degrees of freedom and noncentrality parameter theta.
lkern	character: location kernel, either "Triangle", "Plateau", "Quadratic", "Cubic" or "Gaussian"
aws	logical: if TRUE structural adaptation (AWS) is used.
patchsize	patchsize in case of paws.
memory	logical: if TRUE stagewise aggregation is used as an additional adaptation scheme.
shape	Allows to specify an additional shape parameter for certain family models. Currently only used for family="Variance", that is \chi-Square distributed observations with shape degrees of freedom.
homogeneous	if homgeneous==FALSE and family==Gaussian then create heterogeneous variances according to a chi-squared distribution with number of degrees of freedom given by sphere
varadapt	if varadapt==TRUE use inverse of variance reduction instead of sum of weights in definition of statistical penalty.
ladjust	Factor to increase the default value of lambda
spmin	Determines the form (size of the plateau) in the adaptation kernel. Not to be changed by the user.
seed	Seed value for random generator.
minlevel	Minimum exceedence probability to use in contour plots.
maxz	Maximum of z-scale in plots.
diffz	Gridlength in z
maxni	If TRUE use max_{l<=k}(N_i^{(l)} instead of (N_i^{(k)} in the definition of the statistical penalty.
verbose	If TRUE provide additional information.

Details

Estimates exceedence probabilities

Results for intermediate steps are provided as contour plots. For a good choice of lambda (ladjust) the contours up to probabilities of 1e-5 should be vertical.

Value

A list with components

h	Sequence of bandwidths used
z	seq(0,30,.5), the quantiles exceedence probabilities refer to
prob	the matrix of exceedence probabilities, columns corresponding to h
probna	the matrix of exceedence probabilities for corresponding nonadaptive estimates, columns corresponding to h

Author(s)

Joerg Polzehl [e-mail]

References

S. Becker, P. Mathe, Electron. J. Statist. (2013), 2702-2736, doi:10.1214/13-EJS860

See Also

aws

aws documentation built on Oct. 1, 2024, 1:08 a.m.