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I am fitting a model in the frequency domain, and my fit looks as follows:

[![Model fit][1]][1]Model fit

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful. [1]: https://i.sstatic.net/RzrNz.jpg

I am fitting a model in the frequency domain, and my fit looks as follows:

[![Model fit][1]][1]

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful. [1]: https://i.sstatic.net/RzrNz.jpg

I am fitting a model in the frequency domain, and my fit looks as follows:

Model fit

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful.

broken image fixed (click 'rendered output' or 'side-by-side' to see the difference; image retrieved via Wayback Machine); for more info, see https://gist.github.com/Glorfindel83/9d954d34385d2ac2597bbe864466259f
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edit approved Apr 7, 2019 at 12:08
Glorfindel
edit approved Apr 7, 2019 at 12:08
Glorfindel
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I am fitting a model in the frequency domain, and my fit looks as follows:

Model fit [![Model fit][1]][1]

  

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful. [1]: https://i.sstatic.net/RzrNz.jpg

I am fitting a model in the frequency domain, and my fit looks as follows:

Model fit

 

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful.

I am fitting a model in the frequency domain, and my fit looks as follows:

[![Model fit][1]][1]

 

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful. [1]: https://i.sstatic.net/RzrNz.jpg

replaced http://s3.postimg.org/ with https://s3.postimg.org/
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Community Bot
Community Bot
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I am fitting a model in the frequency domain, and my fit looks as follows:

Model fit http://s3.postimg.org/8aop8zepv/fit.jpgModel fit

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful.

I am fitting a model in the frequency domain, and my fit looks as follows:

Model fit http://s3.postimg.org/8aop8zepv/fit.jpg

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful.

I am fitting a model in the frequency domain, and my fit looks as follows:

Model fit

As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined the residuals and found that a polynomial of the first degree is the missing term in the model function.

What concerns me more is that variance of the data decreases with frequency. What implications does that have on my analysis?

(It seems that looking at the autocorrelation of the residuals also doesn't prove very useful.)

Any ideas would be very helpful.

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