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Re: Filtering data from numerical minimization

  • To: mathgroup at smc.vnet.net
  • Subject: [mg116418] Re: Filtering data from numerical minimization
  • From: Sebastian <sebhofer at gmail.com>
  • Date: Mon, 14 Feb 2011 04:27:29 -0500 (EST)
  • References: <iig75a$rkb$1@smc.vnet.net> <iir4ju$6n3$1@smc.vnet.net>

> My previous post ignored the next line:
>
> > I'd like to keep the rest of the data untouched if possible.
>
> It sounds like you're doing nonlinear regression and you have a fair
> number of dependent-variable outliers, on the order of 20%. The usual
> approach to such situations is to do some form of robust regression,
> that minimizes something other than the unweighted sum of squared
> residuals. There are many possibilities. Can you be more specific
> about your model?

Thanks for your answer. I tried using the algorithm you suggested, but
a simple MovingMedian gives me "nicer" results.
I have to admit that I have no clue what you are talking about in your
second post (I'm not even sure if it really applies to my specific
problem, but I'm happy to learn about it, if it actually does!), so I
just try to clear one thing up:
In my original post I may have not been complete clear about the fact
that my function f is actually a deterministic, analytically given
function, which I try to minimize numerically. The "noise" is
introduced by the minimization which fails to find the correct optimal
value! So another way of solving my problems would be by improving the
way I do the minimization. I just thought that this may not be
possible.
Does this actually provide new information to you...? I'm sorry, I'm a
little lost here...

Thanks for your effort!
Sebastian


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