Re: [R] non-derivative based optimization and standard errors.
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. 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, __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] non-derivative based optimization and standard errors.
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, __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Thomas Lumley Assoc. Professor, Biostatistics [EMAIL PROTECTED] University of Washington, Seattle __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] non-derivative based optimization and standard errors.
You'll really need to give some details if you want anything like a relevant answer. There aren't really general methods for dealing with discontinuous functions you can't compute. A few things come to mind. 1) You might have a look at the literature on segmented regression. Non-differentiable and even discontinuous objective functions arise there. 2) Monte Carlo: you may be able to adapt one of the Monte Carlo optimization approaches to your situation, avoiding having to do Monte Carlo within Monte Carlo. I'd be happy to be more specific if you'll supply details. Reid Huntsinger -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Jean Eid Sent: Thursday, March 24, 2005 9:12 AM To: Spencer Graves Cc: r-help@stat.math.ethz.ch Subject: Re: [R] non-derivative based optimization and standard errors. 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. 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, __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] non-derivative based optimization and standard errors.
Hi Jean, Profiling may be another option and/or finite difference gradients. In any case, if your objective function is discontinuous at some point close to the optimal parameter values, standard errors may not make much sense. Best, Ingmar On 3/24/05 9:12 AM, Jean Eid [EMAIL PROTECTED] 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. 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, __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Ingmar Visser Department of Psychology, University of Amsterdam Roetersstraat 15, 1018 WB Amsterdam The Netherlands http://users.fmg.uva.nl/ivisser/ tel: +31-20-5256735 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] non-derivative based optimization and standard errors.
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, __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
