gevlike
statistics: nlogL = gevlike (params, x)
statistics: [nlogL, acov] = gevlike (params, x)
statistics: [nlogL, acov] = gevlike (params, x, freq)
Negative log-likelihood for the generalized extreme value (GEV) distribution.
nlogL = gevlike (params, x) returns the negative
log likelihood of the data in x corresponding to the GEV distribution
with (1) shape parameter k, (2) scale parameter sigma, and (3)
location parameter mu given in the three-element vector params.
[nlogL, acov] = gevlike (params, x) also
returns the inverse of Fisher’s information matrix, acov. If the input
parameter values in params are the maximum likelihood estimates, the
diagonal elements of acov are their asymptotic variances.
[…] = gevlike (params, x, freq) accepts a
frequency vector, freq, of the same size as x. freq
must contain non-negative integer frequencies for the corresponding elements
in x. By default, or if left empty,
freq = ones (size (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.
MATLAB compatibility. At exactly k = 0 the returned
ACOV deviates from MATLAB’s deliberately. Both implementations agree
to thirteen digits for any k != 0, and both their
k != 0 expressions converge on the values returned here as
k approaches 0; MATLAB’s own k = 0 branch
returns something else entirely, which its k != 0 branch
therefore contradicts. The Gumbel-limit expressions used here are the
limits of the general ones, so ACOV is continuous at 0. This
is independent of the sample size.
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: gevcdf, gevinv, gevpdf, gevrnd, gevfit, gevstat
Source Code: gevlike