mfitstab.elliptical: mfitstab.elliptical

In alphastable: Inference for Stable Distribution

Description

estimates the parameters of a d-dimensional elliptically contoured stable distribution, see Teimouri et al. (2018).

Usage

mfitstab.elliptical(yy, alpha0, Sigma0, Mu0)

Arguments

yy	vector of d-dimensional realizations
alpha0	initial value of the tail index parameter to start the EM algorithm
Sigma0	initial value of the dispersion matrix to start the EM algorithm
Mu0	initial value of the location vector to start the EM algorithm

Value

alpha	estimated value of the tail index parameter
Sigma	estimated value of the dispersion matrix
Mu	estimated value of the location vector

Author(s)

Mahdi Teimouri, Adel Mohammadpour, and Saralees Nadarajah

References

Teimouri, M., Rezakhah, S., and Mohammadpour, A. (2018). Parameter estimation using the EM algorithm for symmetric stable random variables and sub-Gaussian random vectors, Journal of Statistical Theory and Applications, 17(3),1-20,

Examples

# Here we follow for applying the EM algorithm to Z=(x1, x2)^T using the
# initial values alpha0=1, Sigma0=matrix(c(0.75,0.25,0.25,0.75),2,2), and
# Mu0=(0.5,0.5)^T.
library("stabledist")
x1<-urstab(100,1.2,0,1,2,0)
x2<-urstab(100,1.2,0,0.5,2,0)
z<-cbind(x1,x2)
mfitstab.elliptical(z,1,matrix(c(0.75,0.25,0.25,0.75),2,2),c(0.5,0.5))

alphastable documentation built on Sept. 11, 2019, 1:04 a.m.