My apologies for perhaps the stupidity of this question.

Presume that one has the 'frequency response' $Y_{data}(k)$ of a system and also has an estimated model $Y_{syn}(k)$ that fits the data.

How can I quantify the 'goodness of fit' of $Y_{syn}(k)$ to $Y_{data}(k)$ ? Notice that both curves are complex-valued at every instant k (i.e. frequency domain).

For example, assume that I have two estimated models: $Y_{syn}^a(k)$ and $Y_{syn}^b(k)$. My goal (besides visualizing the residual) is to evaluate in a objective way the fit of both models to $Y_{data}(k)$ (the original data set). Is there a single metric to do this?


You could use the $p$-norm of the complex error $E(k)=Y_{data}(k)-Y_{syn}(k)$:


The most common values for $p$ are $p=2$ (Euclidean norm, $l_2$-norm) and $p=\infty$ (maximum norm or Chebyshev norm):

$$\epsilon_2=\sqrt{\sum_{k=1}^K|E(k)|^2}\tag{2}$$ $$\epsilon_{\infty}=\max_{1\le k\le K}\{|E(k)|\}\tag{3}$$

It eventually depends on your application which error measure best reflects the quality of the approximation. Note that for the maximum norm $(3)$, a single index $k$ determines the value of the error measure $\epsilon_{\infty}$, whereas the error measure $\epsilon_2$ in $(2)$ depends on all values of the complex error $E(k)$, and it is proportional to the square root of the mean squared error.

  • 2
    $\begingroup$ And if you have some other knowledge about why you want to weight the error for different $k$, you can include a weighting: $$\epsilon^2_p=\sqrt[p]{\sum_{k=1}^K|w(k)E(k)|^p}$$ This might be useful if you have known values of $k$ for which you need close agreement (little error), so you choose $w$ for those $k$ values to be larger. $\endgroup$ – Peter K. May 19 '16 at 12:36
  • $\begingroup$ @PeterK. Would you consider $1/k$ a possible weighting function, since usually you plot the frequency on a log scale, but the steps in frequency are constant, such that you have a lot more points at high frequencies. And what about normalizing the error ($\left|\frac{E(k)}{Y(k)}\right|^p$), to avoid big impacts of small relative errors at high magnitude values of $Y(k)$ (which is also why you normally plot magnitude in decibel/logarithmic)? $\endgroup$ – fibonatic May 19 '16 at 19:56
  • $\begingroup$ @fibonatic : yes, that makes sense. Sometimes a relative error is what you need. Not sure I'm convinced by $1/k$, but it's certainly possible. $\endgroup$ – Peter K. May 19 '16 at 20:24

You could use common parameters, such as

  • sum-of-squared-residuals $\chi^2$ or the reduced version $\chi^2/N$ ($N$ is the degrees of freedom)
  • coefficient-of-determination $R^2$

Of course, you have to replace every square function by the square of the absolute value, i.e. $(x_i - \mu)^2 = (x_i - \mu)(x_i - \mu)^*$.

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    $\begingroup$ For the sake of clarity, I upvoted your answer as well, but I'm not allowed due to my low rep. $\endgroup$ – WaffleTeX May 19 '16 at 10:52

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