gevfit
Find the maximum likelihood estimator paramhat of the generalized extreme value (GEV) distribution to fit data.
’Display’ = "off"; ’MaxFunEvals’ = 400; ’MaxIter’ = 200; ’TolFun’ = 1e-6; ’TolX’ = 1e-6.
If not provided, the aforementioned values are used by default.
[k_0, sigma, mu]
, where k_0 is the shape
parameter of the GEV distribution, sigma is the scale parameter of the
GEV distribution, and mu is the location parameter of the GEV
distribution.
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 PSI >= 1/2. The GEV distribution is defined for K*(X-MU)/SIGMA > -1.
data = 1:50; [pfit, pci] = gevfit (data); p1 = gevcdf (data, pfit(1), pfit(2), pfit(3)); plot (data, p1) |
See also: gevcdf, gevinv, gevlike, gevpdf, gevrnd, gevstat
Source Code: gevfit
data = 1:50; [pfit, pci] = gevfit (data); p1 = gevcdf (data, pfit(1), pfit(2), pfit(3)); plot (data, p1); |