When we want to grasp the characteristics of the time series signals emitted massively from elect...
When we want to grasp the characteristics of the time series signals emitted massively from electric
power apparatuses or electroencephalogram, and want to decide some diagnoses about the apparatuses or
human brains, we may use some statistical distribution functions. In such cases, the generalized normal
distribution is frequently used in pattern analysts. In assessing the correctness of the estimates of the
shape of the distribution function accurately, we often use a Monte Carlo simulation study; thus, a fast
and efficient random number generation method for the distribution function is needed. However, the
method for generating the random numbers of the distribution seems not easy and not yet to have been
developed. In this paper, we propose a random number generation method for the distribution function
using the the rejection method. A newly developed modified adaptive rejection method works well in the case
of log-convex density functions.