Non Linear Regression Initial Guesses

AI Thread Summary
Making accurate initial guesses for model parameters in nonlinear regression is crucial for achieving a reliable best fit. The user is seeking assistance in deriving noniterative algorithms for initial guesses based on calibration data from an analytical instrument. The Operator's Manual indicates that these initial guesses are calculated automatically, but the user wants to understand the underlying methods. They have consulted resources like the theory of least-squares fitting but have not found the specific information needed. Additional data can be provided if necessary to facilitate the discussion.
scantor145
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Hi:

This is my first post and I'm not sure if this is the right forum. Please redirect if necessary.

I'm new to nonlinear regression, but from what I've read I realize that making "good" initial guesses for the model parameters is very important, otherwise a "best fit" may not result.

I'm using an analytical instrument where the user selects from a short list of mathematical models (please see attachments), a calibration is performed, and the model parameters are calculated. There is no other user input.

This implies, and it is stated in the Operator's Manual, that the initial guesses are calculated using a noniterative algorithm in conjunction with the calibration data.

I would love for someone to show me how to derive theses algorithms for the models attached.

If there is any other information that is required in my quest please let me know. I can supply actual data if necessary.
 

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Bob S said:
Review the theory of least-squares fitting algorithms at
http://mathworld.wolfram.com/LeastSquaresFitting.html
and other web sources.
Bob S

Thanks Bob S.

I went to that site but I can't find anything that would help me derive expressions needed to make initial guesses for different models. Am I missing somethig perhaps?
 
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