[R] non-derivative based optimization and standard errors.

[Thomas Lumley]

On Thu, 24 Mar 2005, Jean Eid wrote:

> The problem is that it is a very complicated model and bootstrap will
> probably take months. The objective function itself is making use of Monte
> Carlo simulation because it is next to impossible to get at a closed form
> solution (of the objective function itself). So I simulate this function
> and get its expectation and match that to data. I thought of doing a
> bootstrap but it will take so much time. I guess if this is the only way,
> then it has to be done.

If the objective function is discontinuous it is entirely possible that 
the bootstrap will not work.  If the bootstrap does work, there are some 
recent methods by LJ Wei and colleagues that avoid some of the 
computation.  I don't know if they will help -- I do remember when 
listening to a talk on the subject that they would only be helpful when 
certain parts of the problem are much harder than others, but I'm not 
sure which parts.

 	-thomas


>
> Jean
>
> On Wed, 23 Mar 2005, Spencer Graves wrote:
>
>>       Have you considered bootstrap or Monte Carlo?
>>
>>       spencer graves
>>
>> Jean Eid wrote:
>>
>>> Hi AlL,
>>>
>>> I ahve this problem that my objective function is discontinous in the
>>> paramaters and I need to use methods such as nelder-mead to get around
>>> this. My question is: How do i compute standard errors to a problem that
>>> does not have  a gradient?
>>>
>>>
>>> Any literature on this is greatly appreciated.
>>>
>>>
>>> Jean,
>>>
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>>
>>
>
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Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle