urstab.trunc: urstab.trunc

In alphastable: Inference for Stable Distribution

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)

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