urstab.trunc {alphastable}

urstab.trunc

Description

using the methodology given by Soltan and Shirvani (2010), Shirvani and Soltani (2013) for simulating iid truncated stable random variable, it simulates truncated stable realizations.

Usage

urstab.trunc(n, alpha, beta, sigma, mu, a, b, param)

Arguments

n

sample size

alpha

tail index parameter

beta

skewness parameter

sigma

scale parameter

mu

location parameter

a

lower bound of truncation

b

upper bound of truncation

param

kind of parameterization; must be 0 or 1 for S_0 and S_1 parameterizations, respectively

Value

a vector of n numeric values

Author(s)

Mahdi Teimouri, Adel Mohammadpour, and Saralees Nadarajah

References

Shirvani, A. and Soltani, A. R. (2013). A characterization for truncated Cauchy random variables with nonzero skewness parameter, Computational Statistics, 28(3), 1011-1016.

Soltani, A. R. and Shirvani, A. (2010). Truncated stable random variables: characterization and simulation, Computational Statistics, 25(1), 155-161.

Teimouri, M. and Nadarajah, S. (2013). On simulating truncated stable random variables, Computational Statistics, 28(5), 2367-2377.

Teimouri, M. and Nadarajah, S. (2017). On simulating truncated skewed Cauchy random variables, Communications in Statistics-Simulation and Computation, 46(2), 1318-1321.

Examples

# We simulate n=200 iid realizations from truncated stable distribution with parameters
# alpha=1.3, beta=0.5, sigma=2, and mu=0 which is truncated over (-5,5) in S_0 parameterization.
urstab.trunc(200,1.3,0.5,2,0,-5,5,0)