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kelly0303

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In summary: Thank you for your reply. So you are saying that fitting the background right (i.e. the tail) is more important than the peak itself for a Voigt profile? If that's the case, then using the inverse error as the weight will help, I just thought it makes more sense to focus on fitting the peak right (and extracting the parameters of the peak) rather than the background.

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kelly0303

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tnich

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kelly0303

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Thank you for your reply! To give more details: I have some data points to which I want to fit a Voigt profile + background. For each data point I have the energy, the counts and the error (square root of the number of counts). In principle, I am just using an already build Python fitting module (lmfit), and one of the options for that is "weights". I assumed that I have to use the value of the errors as the weights to the fit (1/errors), I am just not sure if I should use the absolute or relative error (or something totally different).tnich said:

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tnich

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I can tell you that the data with low counts is the important data for fitting a pdf curve, especially when it is a Voigt profile. In that case you don't know whether you have a Cauchy distribution, a Gaussian distribution, or some combination of the two. So estimating the tails of the distribution is the important aspect, and that depends on fitting the experimental data you have for the tails. Your fit algorithm may take that into account or it may not.

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kelly0303

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Thank you for your reply. So you are saying that fitting the background right (i.e. the tail) is more important than the peak itself for a Voigt profile? If that's the case, then using the inverse error as the weight will help, I just thought it makes more sense to focus on fitting the peak right (and extracting the parameters of the peak) rather than the background.tnich said:

I can tell you that the data with low counts is the important data for fitting a pdf curve, especially when it is a Voigt profile. In that case you don't know whether you have a Cauchy distribution, a Gaussian distribution, or some combination of the two. So estimating the tails of the distribution is the important aspect, and that depends on fitting the experimental data you have for the tails. Your fit algorithm may take that into account or it may not.

Weighting data based on the errors is a statistical technique used to adjust the influence of individual data points in a dataset based on their level of uncertainty or error. This helps to improve the accuracy of the overall analysis and reduce the impact of outliers.

Weighting data based on the errors is important because it allows for a more accurate representation of the data by taking into account the degree of uncertainty associated with each data point. This can lead to more reliable and robust conclusions from the analysis.

The calculation of weighting data based on the errors depends on the specific statistical method being used. In general, it involves assigning a weight to each data point based on its level of uncertainty, and then using these weights in the analysis to give more weight to the more reliable data points.

Weighting data based on the errors can lead to more accurate and precise results in statistical analyses. It can also help to reduce the impact of outliers, which can skew the results of an analysis. Additionally, weighting data can help to account for any systematic errors or biases in the data.

While weighting data based on the errors can improve the accuracy of an analysis, it is not a perfect solution. It relies on accurate estimates of the uncertainty associated with each data point, which may not always be available. Additionally, weighting data can also introduce some bias into the analysis, so it is important to carefully consider the appropriate weighting method for each dataset.

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