Non linear curve fit - parameter accuracy

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SUMMARY

The discussion focuses on estimating the accuracy of parameters obtained from non-linear curve fitting using the Levenberg-Marquardt algorithm. Participants highlight the use of the Jacobian and covariance matrices to derive parameter uncertainties. Specifically, the standard error of the i-th parameter is calculated using the formula σ_i = √(χ²(𝑝)C_{ii}), where C is the variance-covariance matrix derived from the Jacobian. The importance of the mean squared error in assessing fit quality is also emphasized.

PREREQUISITES
  • Understanding of non-linear curve fitting techniques, specifically Levenberg-Marquardt.
  • Familiarity with Jacobian matrices and their role in parameter estimation.
  • Knowledge of covariance matrices and their application in statistical analysis.
  • Basic concepts of mean squared error in the context of model fitting.
NEXT STEPS
  • Study the derivation and application of the Jacobian matrix in non-linear fitting.
  • Learn about the variance-covariance matrix and its significance in parameter estimation.
  • Explore the concept of mean squared error and its calculation in model fitting.
  • Read "Numerical Recipes" for in-depth discussions on non-linear fitting and parameter uncertainty.
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Researchers, data analysts, and statisticians involved in curve fitting and model validation, particularly those utilizing the Levenberg-Marquardt algorithm for parameter estimation.

arwelbath
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Hi,
Suppose i fit some data with a curve, using Levenberg-Marquardt (or equivalent). How do I estimate the accuracy of the parameters (by this i mean p plus or minus something). I've read somewhere that its done using the jacobian and covariance matricies I think, but not sure. Anyone know if there's a 'standard' way of doing this?
Cheers
 
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arwelbath said:
Hi,
Suppose i fit some data with a curve, using Levenberg-Marquardt (or equivalent). How do I estimate the accuracy of the parameters (by this i mean p plus or minus something). I've read somewhere that its done using the jacobian and covariance matricies I think, but not sure. Anyone know if there's a 'standard' way of doing this?
Cheers

most usually you determine the difference between each point you fit and the fitted point and you square all these difference and sum them, you then divide this sum by the number of points you have summed and call this "the mean squared error" (tip: google this).
 
arwelbath said:
Hi,
Suppose i fit some data with a curve, using Levenberg-Marquardt (or equivalent). How do I estimate the accuracy of the parameters (by this i mean p plus or minus something). I've read somewhere that its done using the jacobian and covariance matricies I think, but not sure. Anyone know if there's a 'standard' way of doing this?
Cheers

As I read in an old Origin Help, the standard error of the i-th parameter of a given parameter set p is

\sigma_i=\sqrt{\chi^2(\bold{p})C_{ii}}

where C is the variance-covariance matrix. It is calculated from the Jacobian F (F_{i,j}=\partial f(\bold{p},x_j)/\partial p_i) as
\bold{C}=(\bold{F}^{'} \cdot \bold{F})^{-1}

If you understand this, explain me, please! :smile:

ehild
 
Erm. Thanks gerben for the definition of chi squared. Not quite what I was after.
 
The "Numerical recipes in .." books have a discussion of this. You can find the C book online at

http://www.library.cornell.edu/nr/bookcpdf.html

The second half of chapter 15 discusses non-linear fitting and uncertainty of the estimated parameters
 
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