Data Examples {vstdct} | R Documentation |
Data Examples
Description
example1, example2 and example3 generate i.i.d. vectors from a given distribution with different Toeplitz covariance matrices.
The covariance function \sigma
of the Toeplitz covariance matrix of
example1
: has a polynomial decay,\sigma(\tau)= sd^2(1+|\tau|)^{-gamma}
,example2
: follows anARMA(2,2)
model with coefficients(0.7,-0.4,-0.2,0.2)
and innovations variancesd^2
,example3
: yields a Lipschitz continuous spectral densityf
that is not differentiable, i.e.f(x)= sd^2({|\sin(x+0.5\pi)|^{gamma}+0.45})
Usage
example1(p, n, sd, gamma, family = "Gaussian")
example2(p, n, sd, family = "Gaussian")
example3(p, n, sd, gamma, family = "Gaussian")
Arguments
p |
vector length |
n |
sample size |
sd |
standard deviation |
gamma |
polynomial decay of covariance function for |
family |
distribution of the simulated data. Available distributions are " |
Value
A list containing the following elements:
Y
:pxn
dimensional data matrixsdf
: true spectral density functionacf
: true covariance function
Examples
example1(p=10, n=1, sd=1, gamma=1.2, family="Gaussian")
example2(p=10,n=1,sd=1,family="Gaussian")
example3(p=10, n=1, sd=1, gamma=2,family="Gaussian")