EllKEPT {KEPTED} | R Documentation |
Kernel embedding of probability test for elliptical distribution
Description
This function gives a test on whether the data is elliptically distributed based on kernel embedding of probability. See Tang and Li (2024) for details. Gaussian kernels and product-type inverse quadratic kernels are considered.
Usage
EllKEPT(
X,
eps = 1e-06,
kerU = "Gaussian",
kerTheta = "Gaussian",
gamma.U = 0,
gamma.Theta = 0
)
Arguments
X |
A matrix with n rows and d columns. |
eps |
The regularization constant added to the diagonal to avoid
singularity. Default value is |
kerU |
The type of kernel function on |
kerTheta |
The type of kernel function on |
gamma.U |
The tuning parameter |
gamma.Theta |
The tuning parameter |
Details
The Gaussian kernel is defined as k(z1,z2)=exp(-gamma*||z1-z2||^2), and the
Product-type Inverse-Quadratic (PIQ
) kernel is defines as
k(z1,z2)=Prod_j(1/(1+gamma*(z1_j-z2_j)^2)). The recommended procedure of
selecting tuning parameter is given as in the simulation section of Tang and
Li (2023+), where we set
1/sqrt(gamma)=(n(n-1)/2)^(-1)*sum_{1<=i<j<=n}||Z_i-Z_j||.
Value
A list of the following:
stat |
The value of the test statistic. |
pval |
The p-value of the test. |
lambda |
The |
gamma.U |
The tuning parameter |
gamma.Theta |
The tuning parameter |
Note
In the arguments, eps
refers to a regularization constant added to
the diagonal. When the dimension is high, we recommend increasing eps
to avoid singularity.
References
Tang, Y. and Li, B. (2024), “A nonparametric test for elliptical distribution based on kernel embedding of probabilities,” https://arxiv.org/abs/2306.10594
Examples
set.seed(313)
n=50
d=3
## Null Hypothesis
X=matrix(rnorm(n*d),nrow=n,ncol=d)
EllKEPT(X)
## Alternative Hypothesis
X=matrix(rchisq(n*d,2)-2,nrow=n,ncol=d)
EllKEPT(X)