How can I resample data with errors linearly in log space?

In summary, the person is looking to resample a set of data and its errors linearly in log space, with the same number of points. They are considering interpolating between points to get the data but are unsure how to calculate the errors. The other person suggests treating the errors the same way as the original data when interpolating. They also mention that it may not be legitimate to interpolate experimental errors, but the person clarifies that the data is from numerical simulations and it is a common practice in astronomy to rebin from linear to logarithmic while maintaining signal to noise. They provide a link to a package they have used for this purpose.
  • #1
Veles
7
0
I need to resample a set of data and its errors linearly in log space, with the same number of points. I was just going to interpolate between points to get the data - but how do I calculate the errors?
 
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  • #2
what do you mean with "resample" if then you talk about interpolating them?
 
  • #3
If you're interpolating data points, why not interpolate their errors? The errors produce two additional sets of data points above/below the original data. Treat them the same way you're treating your original data.

I assume the "data" are from a numerical simulation? Otherwise it is probably not legit to interpolate experimental errors.
 
  • #4
Its experimental errors in astronomy in a spectra. It seems to be quite a commonly done thing - rebinning from linear wavelength to logarithmic whilst maintaining the overall signal to noise. Given that it is "non legit" I should have probably asked in the physics rather than maths forum!

For example I have done it using this IRAF package. http://drforum.gemini.edu/wp-content/uploads/2014/05/README.txt
 

1. What is resampling data with errors?

Resampling data with errors is a statistical technique used to estimate the uncertainty or variability in a dataset by creating multiple samples from the original data and analyzing the results.

2. Why is resampling data with errors important?

Resampling data with errors is important because it allows for a more accurate estimation of the true values of a dataset, taking into account the inherent errors and variability in the data. This can lead to more reliable and robust conclusions in statistical analyses.

3. What types of errors can be accounted for in resampling data?

Resampling data with errors can account for both random and systematic errors. Random errors refer to the natural variability in a dataset, while systematic errors refer to biases or inaccuracies in the data collection process.

4. How does resampling data with errors work?

In resampling data with errors, multiple samples are created from the original dataset by randomly selecting data points with replacement. Each sample is then analyzed and the results are combined to provide an estimate of the true values and their uncertainty.

5. What are the advantages of resampling data with errors compared to traditional statistical methods?

Resampling data with errors has several advantages over traditional statistical methods. It does not make any assumptions about the underlying distribution of the data, it can handle complex datasets, and it provides a more accurate estimation of uncertainty. It also allows for the incorporation of error estimation in a wide range of statistical analyses.

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