Regression in Matlab assuming Student's t Distributed Error Terms

I see that it is possible to use regress/regstats for OLS, and I found an online implementation of L1-Regression (Laplace), but I can't quite seem to figure out how to implement t distributed error terms. I have tried maximizing the log-likelihood of the residuals, but don't seem to be coming up with the right answer.

classdef student < handle
   methods (Stat开发者_JAVA技巧ic)

       % Find the sigma that maximizes the Log Liklihood function given a B
       function s = findLonS(r,df)
           n = length(r);

           % if x ~ t location, scale distribution with df 
           % degrees of freedom, then (x-u)/sigma ~ t(df)
           f = @(s) -sum(log(tpdf(r ./ s, df)));

           s = fminunc(f, (r'*r)/n);
       end

       function B = regress(X,Y,df) 
           [n,m] = size(X);

           bInit = ones(m, 1);

           r = (Y - X*bInit);
           s = student.findLonS(r, df);

           % if x ~ t location, scale distribution with df 
           % degrees of freedom, then (x-u)/sigma ~ t(df)
           f = @(b) -sum(log(tpdf((Y - X*b) ./ s, df)));

           options = optimset('MaxFunEvals', 10000, 'TolX', 1e-16, 'TolFun', 1e-16);
           [B, fval] = fminunc(f, bInit, options); 
       end
   end
end

Comparing to an R implementation (which I know has been tested and is accurate), the solutions I am getting to this is wrong.

Any suggestions for fixing or ideas where I could find a solution already available?

my guess would be you have to adjust the scale s for the given b. This would either mean doing something like alternatively optimizing b, then adjusting s, and optimizing b again, or possibly rewriting your objective as

f = @(b)(-sum(log(tpdf((Y-X*b) ./ student.findLonS(Y-X*b,df),df))));