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From Luc Maisonobe <Luc.Maison...@free.fr>
Subject Re: [math] Standard errors in estimated parameters using LevenbergMarquardtEstimator
Date Wed, 12 Dec 2007 22:00:36 GMT
luc.maisonobe@free.fr a écrit :
> Selon koshino kazuhiro <koshino@ri.ncvc.go.jp>:
> 
>> Hello,
>>
>> Can I get standard errors in estimated parameters using
>> GaussNewtonEstimator or LevenbergMarquardtEstimator?
> 
> Yes. All estimators implement the Estimator interface which includes a getRMS
> method in addition to the estimate method.

Oooops. Sorry, my answer does not match your question. You asked about 
parameters and I answered about observations (measurements).

So the proper answer is no, there is no way to get any information about 
errors on parameters yet. One method to have an estimate of the quality 
of the result is to check the eigenvalues related to the parameters. 
Perhaps we could look at this after having included a Singular Value 
Decomposition algorithm ? Is this what you want or do you have some 
other idea ?

Luc

> the method (because it is your problems which defines both the model, the
> parameters and the observations).
> If you call it before calling estimate, it will use the initial values of the
> parameters, if you call it after having called estimate, it will use the
> adjusted values.
> 
> Here is what the javadoc says about this method:
> 
>    * Get the Root Mean Square value, i.e. the root of the arithmetic
>    * mean of the square of all weighted residuals. This is related to the
>    * criterion that is minimized by the estimator as follows: if
>    * <em>c</em> is the criterion, and <em>n</em> is the number
of
>    * measurements, then the RMS is <em>sqrt (c/n)</em>.
>> I think that those values are very important to validate estimated
>> parameters.
> 
> It may sometimes be misleading. If your problem model is wrong and too
> "flexible", and if your observation are bad (measurements errors), then you may
> adjust too many parameters and have the bad model really follow the bad
> measurements and give you artificially low residuals. Then you may think
> everything is perfect which is false. This is about the same kind of problems
> knowns as "Gibbs oscillations" for polynomial fitting when you use a too high
> degree.
> 
> Luc
> 
>> Is use of classes in java.lang.reflect.* only way to get standard errors?
>>
>> Kind Regards,
>>
>> Koshino
>>
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> 
> 
> 
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