Statistics: gumbelfit

### Function Reference: `gumbelfit`

statistics: paramhat = gumbelfit (x)
statistics: [paramhat, paramci] = gumbelfit (x)
statistics: [paramhat, paramci] = gumbelfit (x, alpha)
statistics: […] = gumbelfit (x, alpha, censor)
statistics: […] = gumbelfit (x, alpha, censor, freq)
statistics: […] = gumbelfit (x, alpha, censor, freq, options)

Estimate parameters and confidence intervals for Gumbel distribution.

`paramhat = gumbelfit (x)` returns the maximum likelihood estimates of the parameters of the Gumbel distribution (also known as the extreme value or the type I generalized extreme value distribution) given in x. `paramhat(1)` is the location parameter, mu, and `paramhat(2)` is the scale parameter, beta.

`[paramhat, paramci] = gumbelfit (x)` returns the 95% confidence intervals for the parameter estimates.

`[…] = gumbelfit (x, alpha)` also returns the `100 * (1 - alpha)` percent confidence intervals for the parameter estimates. By default, the optional argument alpha is 0.05 corresponding to 95% confidence intervals. Pass in `[]` for alpha to use the default values.

`[…] = gumbelfit (x, alpha, censor)` accepts a boolean vector, censor, of the same size as x with `1`s for observations that are right-censored and `0`s for observations that are observed exactly. By default, or if left empty, `censor = zeros (size (x))`.

`[…] = gumbelfit (x, alpha, censor, freq)` accepts a frequency vector, freq, of the same size as x. freq typically contains integer frequencies for the corresponding elements in x, but it can contain any non-integer non-negative values. By default, or if left empty, `freq = ones (size (x))`.

`[…] = gumbelfit (…, options)` specifies control parameters for the iterative algorithm used to compute the maximum likelihood estimates. options is a structure with the following field and its default value:

- `options.TolX = 1e-6`

The Gumbel distribution is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions. This version is suitable for modeling maxima. For modeling minima, use the alternative extreme value fitting function, `evfit`.

Further information about the Gumbel distribution can be found at https://en.wikipedia.org/wiki/Gumbel_distribution

See also: gumbelcdf, gumbelinv, gumbelpdf, gumbelrnd, gumbellike, gumbelstat, evfit

Source Code: gumbelfit

### Example: 1

     ## Sample 3 populations from different Gumbel distibutions
     rand ("seed", 1);    # for reproducibility
     r1 = gumbelrnd (2, 5, 400, 1);
     rand ("seed", 11);    # for reproducibility
     r2 = gumbelrnd (-5, 3, 400, 1);
     rand ("seed", 16);    # for reproducibility
     r3 = gumbelrnd (14, 8, 400, 1);
     r = [r1, r2, r3];

     ## Plot them normalized and fix their colors
     hist (r, 25, 0.32);
     h = findobj (gca, "Type", "patch");
     set (h(1), "facecolor", "c");
     set (h(2), "facecolor", "g");
     set (h(3), "facecolor", "r");
     ylim ([0, 0.28])
     xlim ([-11, 50]);
     hold on

     ## Estimate their MU and BETA parameters
     mu_betaA = gumbelfit (r(:,1));
     mu_betaB = gumbelfit (r(:,2));
     mu_betaC = gumbelfit (r(:,3));

     ## Plot their estimated PDFs
     x = [min(r(:)):max(r(:))];
     y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
     plot (x, y, "-pr");
     y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
     plot (x, y, "-sg");
     y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
     plot (x, y, "-^c");
     legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
              "Normalized HIST of sample 2 with μ=-5 and β=3", ...
              "Normalized HIST of sample 3 with μ=14 and β=8", ...
              sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
                      mu_betaA(1), mu_betaA(2)), ...
              sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
                      mu_betaB(1), mu_betaB(2)), ...
              sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
                      mu_betaC(1), mu_betaC(2))})
     title ("Three population samples from different Gumbel distibutions")
     hold off