Generalized extreme value probability density function
Y = gevpdf(X,k,sigma,mu)
Y = gevpdf(X,k,sigma,mu) returns the pdf of the generalized extreme value (GEV) distribution with shape parameter k, scale parameter sigma, and location parameter, mu, evaluated at the values in X. The size of Y is the common size of the input arguments. A scalar input functions as a constant matrix of the same size as the other inputs.
Default values for k, sigma, and mu are 0, 1, and 0, respectively.
When k < 0, the GEV is the type III extreme value distribution. When k > 0, the GEV distribution is the type II, or Frechet, extreme value distribution. If w has a Weibull distribution as computed by the wblpdf function, then -w has a type III extreme value distribution and 1/w has a type II extreme value distribution. In the limit as k approaches 0, the GEV is the mirror image of the type I extreme value distribution as computed by the evcdf function.
The mean of the GEV distribution is not finite when k ≥ 1, and the variance is not finite when k ≥ 1/2. The GEV distribution has positive density only for values of X such that k*(X-mu)/sigma > -1.
 Embrechts, P., C. Klüppelberg, and T. Mikosch. Modelling Extremal Events for Insurance and Finance. New York: Springer, 1997.
 Kotz, S., and S. Nadarajah. Extreme Value Distributions: Theory and Applications. London: Imperial College Press, 2000.