All statistical methods rely on assumptions to some extent. Two assumptions frequently met in statistical analyses are those of normal distribution and independence. When examining robustness properties of such assumptions by Monte Carlo simulations it is therefore crucial that the possible effects of autocorrelation and non-normality are not confounded so that their separate effects may be investigated. This article presents a number of non-normal variables with non-confounded autocorrelation, thus allowing the analyst to specify autocorrelation or shape properties while keeping the other effect fixed.
"Simulation of Non-normal Autocorrelated Variables,"
Journal of Modern Applied Statistical Methods:
2, Article 15.
Available at: http://digitalcommons.wayne.edu/jmasm/vol5/iss2/15