from Anderson-Darling Goodness Of Fit Test to Inverse Gaussian Distbtn by Matthew Brenneman
Tests M random samples of N random vars to determine if they are from Inverse Gaussian distbtn.

Subr_ComputeADTestStat(Mu,Lambda,G)
function [AD] = Subr_ComputeADTestStat(Mu,Lambda,G)
%
%
global M N
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

AD = zeros(1,M);
%
for ii = 1:M
    %   a) Compute CDF values for IG distbtd rvs and order the values
    z = zeros(1,N);
    Z = zeros(1,N);
    for jj = 1:N
        z(1,jj) = Subr_ComputeIgCDF(G(ii,jj),Lambda(1,ii),Mu(1,ii));
    end
    Z = sort(z,'ascend');
    %
    %   b) Compute AD Statistic
    ADsum = 0;
    for jj = 1:N
        Index1 = jj;
        Index2 = N - jj + 1;
        Q1 = log(Z(1,Index1));
        Q2 = log(1-Z(1,Index2));
        ADsum = ADsum + (2*jj-1)*(Q1 + Q2);
    end
    AD(1,ii) = -N - ADsum/N;
    %
    clear z Z
end

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