Hi list,
my previous question was obviously too basic to deserve an answer - apologies for that. I'm learning, things can only get better :-)
My current problem is with fitting a generalized gamma distribution with an additional "shift" parameter, that represents a shift of the distribution along the X axis.
The gnlr3 function (in Jim Lindsey's GNLM package) fits this distribution in this form:
f(y) = fy^(f-1)/((m/s)^(fs) Gamma(s)) y^(f(s-1)) exp(-(y s/m)^f) (1)
I would like to include a fourth parameter, say u, like this:
f(y) = fy^(f-1)/((m/s)^(fs) Gamma(s)) (y-u)^(f(s-1)) exp(-((y-u) s/m)^f) (2)
My best idea so far is to iteratively fit expression (1), each time shifting the data with an amount u. Plotting the maximum likelihood of the fit against u should give me an idea of where the optimum value for u is. Of course, this procedure will take quite some time, and will not be very straightforward since the generalized gamma shows convergence problems without good initial estimates...
Any suggestions for a better approach?
Thanks in advance, Arnout
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