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How, if at all, can one compare the "fit" of a simple linear vs. non-linear regression model to observed data?

I apologize if I didn't search long/hard enough for the answer, but I cannot find anything concrete.

-Patrick

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  • $\begingroup$ What software are you using? $\endgroup$
    – Michelle
    Jan 26, 2012 at 23:39
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    $\begingroup$ Can you share your purpose in comparing the fit of the different models? To develop a predictive model? To describe a particular data set? To provide evidence for or against a particular theory? You might choose different comparison approaches depending on your purpose. $\endgroup$
    – Anne Z.
    Jan 27, 2012 at 3:05
  • $\begingroup$ Anne, at this point it is simply for description. I would like to quantitatively be able to say that a non-linear fit is preferred. $\endgroup$
    – Patrick
    Jan 28, 2012 at 17:21
  • $\begingroup$ Michelle, I use R. $\endgroup$
    – Patrick
    Feb 3, 2012 at 17:20
  • $\begingroup$ Would using AIC/BIC or the LogLik be sound statistically to make a choice between non-linear and linear models on the same dataset? $\endgroup$
    – user9974
    Mar 19, 2012 at 23:33

1 Answer 1

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Perform cross-validation on each model on a development set to find the best hyper-parameters / accuracy estimate for each. Then check that the accuracy estimates are still valid on some held out data that you did not use during the model selection / parameter tuning phase.

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