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If I take the Fourier transform of data $x \pm \sigma$, is there a standard approach to what the error in the outputs will be? Would the best way be a direct evaluation of the upper and lower bounds?

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  • $\begingroup$ The Fourier transform is linear - the error in Fourier domain should thus be the Fourier transform of the error in x domain. $\endgroup$ – AlexE Jan 25 '18 at 5:41
  • $\begingroup$ If sigma is understood as a spread or variance instead of as a function of x, you can use the Fourier transform's uncertainty relation. $\endgroup$ – AlexE Jan 25 '18 at 22:11
  • $\begingroup$ @AlexE would you consider converting your comment into an answer? also, you might want to mention this post where this is demonstrated on a particular example with some python code. $\endgroup$ – Anton Menshov May 19 at 6:31
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Assembled from comments of @AlexE:


The Fourier transform is linear, so the error in the Fourier domain is the Fourier transform of the error in the spatial (original) domain.

So, if $\sigma$ is understood as a variance spread not being a function of $x$, one can use the Fourier transform's uncertainty relation.

This StackOverflow post demonstrates this behaviour using Python code.

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    $\begingroup$ I don't think that appealing just to linearity is enough, shouldn't the conditioning / condition number be mentioned here? That said, the Fourier transform is unitary (condition number of 1) so I don't disagree with the conclusion. $\endgroup$ – rchilton1980 Jun 11 at 19:42
  • $\begingroup$ @rchilton1980 I agree. I would be happy for a reference with some rigorous analysis that I can read without diving into deep derivations. $\endgroup$ – Anton Menshov Jun 12 at 21:22
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I've pondered this question before. The best I can come up with is as follows.

The Fourier transform y = fft(x) can be expressed as some matrix, $X$, dot producted with $x$.

See scipy documentation examples for how to generate the Fourier matrix here

This matrix representation means that the Fourier transform can be thought of as a linear least squares problem. That is, the Fourier coefficients are the fit parameters. The problem of estimating the fit parameters' standard deviation has a known known solution.

See here the wikipedia article hereUnbiasedness and variance of $\beta$ for how to do so.

For the sake of completeness, the quantity on wishes to find is the standard deviation of the fit parameters, $\sigma_\beta$.

Using the wikipedia article above

$\sigma_\beta^2 =E[(\hat{\beta}-\beta)(\hat{\beta}-\beta)] = \sigma \sigma^T (X^T X)^{-1}$

Where $X$ is the fourier matrix.

Note that $\sigma \sigma^T$ is a covariance matrix, not a scalar. In your case, it will most likely be a diagonal matrix

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