📄 probit_gd.m
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% PURPOSE: demo of probit_g
% Gibbs sampling for probit heteroscedastic estimation
%---------------------------------------------------
% USAGE: probit_gd
%---------------------------------------------------
clear all;
n=100;
k = 3;
evec = randn(n,1);
tt=1:n;
x = randn(n,k);
x(1:n,1) = ones(n,1);
b = ones(k,1);
b(3,1) = -2.0;
b(2,1) = 2.0;
b(1,1) = -0.5;
y = x*b + 0.2*evec;
yc = zeros(n,1);
% now censor the data
for i=1:n
if y(i,1) > 0
yc(i,1) = 1;
else
yc(i,1) = 0;
end;
end;
% add outliers
%x(50,2) = 5;
%x(75,3) = 5;
Vnames = strvcat('y','constant','x1','x2');
prior.rval = 40; % heteroscedastic prior
ndraw = 1100;
nomit = 100;
result = probit_g(yc,x,ndraw,nomit,prior);
plot(tt,result.vmean);
title('vi-estimates');
pause;
prt(result,Vnames);
tt=1:n;
[ys yi] = sort(result.y);
plot(tt,ys,tt,result.yhat(yi,1),'--');
plot(tt,result.ymean,tt,y,'o');
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