[R] MLE where loglikelihood function is a function of numerical solutions

[Albyn Jones]

to clarify: by  "if you knew that LL(psi+eps) were well approximated  
by LL(psi), for the values of eps used to evaluate numerical  
derivatives of LL. "
I mean the derivatives of LL(psi+eps) are close to the derivatives of LL(psi),
and perhaps you would want the hessian to be close as well.

albyn

Quoting Albyn Jones <jones at reed.edu>:

> Hi Kristian
>
> The obvious approach is to treat it like any other MLE problem:  evaluation
> of the log-likelihood is done as often as necessary for the  
> optimizer you are using: eg a call to optim(psi,LL,...)  where  
> LL(psi) evaluates the log likelihood at psi.  There may be  
> computational shortcuts that would work if you knew that LL(psi+eps)  
> were well approximated by LL(psi), for the values of eps used to  
> evaluate numerical derivatives of LL.  Of course, then you might  
> need to write your own custom optimizer.
>
> albyn
>
> Quoting Kristian Lind <kristian.langgaard.lind at gmail.com>:
>
>> Hi there,
>>
>> I'm trying to solve a ML problem where the likelihood function is a function
>> of two numerical procedures and I'm having some problems figuring out how to
>> do this.
>>
>> The log-likelihood function is of the form L(c,psi) = 1/T sum [log (f(c,
>> psi)) - log(g(c,psi))], where c is a 2xT matrix of data and psi is the
>> parameter vector. f(c, psi) is the transition density which can be
>> approximated. The problem is that in order to approximate this we need to
>> first numerically solve 3 ODEs. Second, numerically solve 2 non-linear
>> equations in two unknowns wrt the data. The g(c,psi) function is known, but
>> dependent on the numerical solutions.
>> I have solved the ODEs using the deSolve package and the 2 non-linear
>> equations using the BB package, but the results are dependent on the
>> parameters.
>>
>> How can I write a program that will maximise this log-likelihood function,
>> taking into account that the numerical procedures needs to be updated for
>> each iteration in the maximization procedure?
>>
>> Any help will be much appreciated.
>>
>>
>> Kristian
>>
>> 	[[alternative HTML version deleted]]
>>
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>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>