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https://issues.apache.org/jira/browse/MATH-176?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12557998#action_12557998
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Kazuhiro Koshino commented on MATH-176:
---------------------------------------

Sorry for late reply.

Degrees of freedom (dof) is defined by
 n - p
, where n is a number of measurements, p is a number of parameters, 
respectively. 
However, 
if n is equal to p, there is no freedom. It means that measurements determine 
parameters uniquely and no need to validate the model (parameter problem).
If n < p, we cannot determine the parameters represent the measurements because 
there are infinite number of parameter sets. For example, we want to represent 
a measurement with y = ax + b,
where a and b are parameters, y is a measurement for an input x. There are 
infinite straight lines passing through the point (x, y).

For your example of 2x = 3, n = 1 (a measurement = 3 for an input value of 2) 
and p = 1 (parameter is x). Then the dof is 0.

If users will get errors in estimated parameters for the cases of n = p or n < 
p, My idea is
1) AbstractEstimator throws EstimationException during calling 
guessParametersErrors.
or
2) An additional method, which enables users to check the validity of the dof, 
that is, n > p befor calling guessParametersErrors

My english is too poor. I hope that you will ask me any questions in my comment 
patiently.

> Getting errors in estimated parameters using Estimator
> ------------------------------------------------------
>
>                 Key: MATH-176
>                 URL: https://issues.apache.org/jira/browse/MATH-176
>             Project: Commons Math
>          Issue Type: Improvement
>    Affects Versions: 1.2
>            Reporter: Kazuhiro Koshino
>            Assignee: Luc Maisonobe
>            Priority: Minor
>             Fix For: 1.2
>
>         Attachments: LevenbergMarquardtEstimator.java.diff
>
>
> To validate estimated parameters by GaussNewtonEstimator and 
> LevenbergMarquardtEstimator, we need errors (or covariances) in the 
> parameters.

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