In addition to other answers,
STB-38 sg71 . . . . . . . . . . . . . . . . Routines to maximize a function
(help amoeba if installed) . . . . . . . . . . . . . . . . C. Ferrall
7/97 pp.22--26; STB Reprints Vol 7, pp.176--181
routines to maximize a multi-dimensional function
STB-32 sg56 . . . . Implementation of a simplex-based maximization algorithm
(help simplex if installed) . . . . . . . . . . . . . . . . . T. Barr
7/96 pp.23--27; STB Reprints Vol 6, pp.134--140
likelihood-maximization routine based on the simplex method,
also known as the Amoeba method
Nick
[email protected]
Pablo Mitnik
> I need to do numerical optimization for a function that is not a
> likelihood function but a (rather complex) loss function. Given that
> stata has the command ml, and that more in general it uses
> algorithms of
> numerical optimization in many other commands as well, I guess that I
> should be able to find the vector of parameters that minimize my loss
> function by simply writing an appropriate evaluator and using an
> existing command to call it. But I haven't been able to
> figure out what
> command/s to use. I thought of "tricking" ml (d0) by giving
> it minus my
> loss function as if it were log L, but the way in which (in my limited
> understanding) ml works, with the thetas defined as linear
> functions of
> the parameters, seems to preclude this alternative (my loss function,
> which would produce the scalar lnf, is not linear on the parameters).
>
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