Generalized extreme value parameter estimates
parmhat = gevfit(X)
[parmhat,parmci] = gevfit(X)
[parmhat,parmci] = gevfit(X,alpha)
[...] = gevfit(X,alpha,options)
parmhat = gevfit(X) returns maximum likelihood estimates of the parameters for the generalized extreme value (GEV) distribution given the data in X. parmhat(1) is the shape parameter, k, parmhat(2) is the scale parameter, sigma, and parmhat(3) is the location parameter, mu.
[...] = gevfit(X,alpha,options) specifies control parameters for the iterative algorithm used to compute ML estimates. This argument can be created by a call to statset. See statset('gevfit') for parameter names and default values. Pass in  for alpha to use the default values.
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 wblfit 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 evfit 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 is defined for 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.