For each element of x, compute the cumulative distribution function (CDF) of the GEV distribution with shape parameter k, scale parameter sigma, and location parameter mu. The size of p is the common size of x, k, sigma, and mu. A scalar input functions as a constant matrix of the same size as the other inputs.

[…] = gevcdf (x, k, sigma, mu, "upper") computes the upper tail probability of the GEV distribution with parameters k, sigma, and mu, at the values in x.

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 wblcdf 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.

Further information about the generalized extreme value distribution can be found at https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution

See also: gevinv, gevpdf, gevrnd, gevfit, gevlike, gevstat

Source Code: gevcdf