Simulates from p-gamma distributions. These are p-RDTS distributions with alpha=0.
rPGamma(n, t, mu, p, step = 1)Returns a vector of n random numbers.
Number of observations.
Parameter >0.
Parameter >0.
Parameter >1.
Tuning parameter. The larger the step, the slower the rejection sampling, but the fewer the number of terms. See Hoefert (2011) or Section 4 in Grabchak (2019).
Michael Grabchak and Lijuan Cao
Uses Theorem 1 in Grabchak (2021) to simulate from a p-Gamma distribution. This distribution has Laplace transform
L(z) = exp( t int_0^infty (e^(-xz)-1)e^(-(mu*x)^p) x^(-1) dx ), z>0
and Levy measure
M(dx) = t e^(-(mu*x)^p) x^(-1) 1(x>0)dx.
M. Grabchak (2019). Rejection sampling for tempered Levy processes. Statistics and Computing, 29(3):549-558
M. Grabchak (2021). An exact method for simulating rapidly decreasing tempered stable distributions. Statistics and Probability Letters, 170: Article 109015.
M. Hofert (2011). Sampling exponentially tilted stable distributions. ACM Transactions on Modeling and Computer Simulation, 22(1), 3.