[SciPy-user] nonlinear fit with non uniform error?
David Huard
david.huard@gmail....
Thu Jun 21 09:19:29 CDT 2007
Trevis,
2007/6/21, Trevis Crane <t_crane@mrl.uiuc.edu>:
>
> As an aside, will those of you who are **more** in the know on this topic
> than the rest of us suggest a good text that has a worthwhile treatment of
> this subject (as well as other related data analysis/statistical issues)?
>
My bible is Probability Theory : The Logic of Science by E. T. Jaynes.
http://omega.albany.edu:8008/JaynesBook.html
It's not so much a book about optimization and fitting than on the general
principles of probability. It was worth the reading time though.
There is a paper in the hydrological literature (Sorooshian and Dracup,
water resources research, vol.16, no.2, 1980) that discusses the calibration
of hydrologic models in correlated and heteroscedastic error cases. I guess
every discipline has a paper similar to this one but this is the one I know.
There is also a Book by A. Zellner, An Introduction to Bayesian Inference in
Econometrics, 1971 that I found helpful.
As you can see, I'm not aware of a comprehensive treatise on the subject. I
just picked up bits from different articles.
HTH,
David
I'd love to learn more about it, but just jumping on Amazon and picking a
> book at almost random seems like a good way to waste a lot of money I don't
> have on books that I don't need, so if you have a favorite reference or
> text, I'm interested in knowing about it.
>
>
>
> thanks,
>
> trevis
>
>
>
> -----Original Message-----
> *From:* scipy-user-bounces@scipy.org [mailto:scipy-user-bounces@scipy.org]
> *On Behalf Of *David Huard
> *Sent:* Thursday, June 21, 2007 8:09 AM
> *To:* SciPy Users List
> *Subject:* Re: [SciPy-user] nonlinear fit with non uniform error?
>
>
>
> Hi,
>
> What you have is an heteroscedastic normal distribution (varying variance)
> describing the residuals.
>
> 2007/6/21, Matthieu Brucher <matthieu.brucher@gmail.com>:
>
> 1)Does this mean that least squares is NOT ok?
>
> Yes, LS is _NOT_ OK because it assumes that the distribution (with its
> parameters) is the same for all errors. I don't remember exactly, but this
> may be due to ergodicity
>
>
> Well, let's put things in perspective. You can still use ordinary
> least-squares. Theoretically, this means you're making the assumption that
> the error mean and variance are fixed and constant. In your case, this is
> not true and you can consider the LS solution like an approximation. What
> will happen under this approximation is that large errors on Cy will tend to
> dominate the residuals, and values in Ay will probably not be fitted
> optimally. I advise you try it anyway and visually check whether you care
> about that or not.
>
> 2)What does "rescaling" mean in this context?
>
>
>
> You must change B and C so that :
> Ay +/- 5
> B'y +/- 5
> C'y +/- 5
>
>
> Or maximize the likelihood of a multivariate normal distribution, whose
> covariance matrix describes your assumption about the heteroscedasticity of
> the residuals.
>
> \Sigma =
> | \sigma_A^2 0 0 |
> | 0 \sigma_B^2 0 |
> | 0 0 \sigma_C^2 |
>
> Heteroscedastic likelihood = -n/2 \ln(2\pi) - 1/2 \sum \ln(\sigma_i^2)
> -1/2 \sum \sigma_i^{-2} (y_{obs} - y_{sim})^2
>
>
> You might also consider the possibility that your errors are
> multiplicative rather than additive. In this case, describing the residuals
> by a lognormal distribution could make more sense.
>
> Maximize lognormal likelihood: L=lognormal(y_sim | ln(y_obs), \sigma)
>
> Cheers,
>
> David
>
> Matthieu
>
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