Data weighting on semi log scale

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SUMMARY

The discussion focuses on performing a fitting procedure on a semi-logarithmic scale for analyzing the decay of luminescence in crystals using the equation I = (1/τ) e^(-t/τ) (-Li₂(-erf(√(t/τ))))/erf(√(t/τ)). The adviser recommends weighting the experimental data points using the inverse of their values or the square of the inverse to improve parameter precision. Additionally, an alternative method suggested is to take the logarithm of the measured values and perform a fit with equal weighting. These approaches ensure more accurate fitting results in the presence of nonlinearities.

PREREQUISITES
  • Understanding of semi-logarithmic scales
  • Familiarity with exponential decay functions
  • Knowledge of dilogarithm and error functions (Li₂ and erf)
  • Experience with data weighting techniques in statistical fitting
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  • Research data weighting techniques in nonlinear regression analysis
  • Learn about the properties and applications of the dilogarithm function
  • Explore methods for fitting data on semi-logarithmic scales
  • Investigate the implications of using logarithmic transformations in data analysis
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Researchers and scientists studying luminescence decay, data analysts performing nonlinear regression, and anyone interested in advanced statistical fitting techniques on semi-logarithmic scales.

raul_l
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Hi

I have to perform a fitting procedure on a semi logarithmic scale but I'm not sure how to weigh the experimental data points. I'm studying the decay of the luminescence of a certain type of crystals and the function I'm using has the form

[tex]I = \frac{1}{\tau} e^{-\frac{t}{\tau}} \frac{-Li_{2}(-erf(\sqrt{\frac{t}{\tau}})}{erf(\sqrt{\frac{t}{\tau}})}[/tex]

where [tex]Li_{2}[/tex] and erf are the dilogarithm and error functions. (for simplicity I've omitted some constants). Basically, it's more or less a pure exponential with some nonlinearities at the beginning.

My adviser said that I should try to weigh each data point with the inverse of its value (or the square of inverse). Otherwise I get imprecise values for some of the parameters that depend on the smaller values of the data points. But I'm not sure if this is the right way to do this. It just doesn't feel right.

I was wondering if there are any general rules/guidelines for choosing the correct weights on a semi logarithmic scale.
 
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Your advisor is correct. Alternatively, you could take the log of the measured values and do a fit to them, with equal weighting.
 

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