A Standard error of the coefficient of variation

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The discussion centers on the standard error of the coefficient of variation (CV) specifically in the context of an exponential distribution. A formula for calculating the standard error is provided, which involves the CV and the number of observations. Participants express uncertainty about the applicability of this formula to the exponential distribution. Clarification on whether the formula is valid for this distribution is sought. The conversation highlights the need for further exploration of statistical principles related to the coefficient of variation.
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What is the standard error of the coefficient of variation in an exponential distribution?
What is the standard error of the coefficient of variation in an exponential distribution?
 
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What do you think it is?
 
hutchphd said:
What do you think it is?
On

https://influentialpoints.com/Training/standard_error_of_coefficient_of_variation.htm

I read:
$$SE(CV) \, = \, \frac{CV}{\sqrt{2 \, n}} \, \sqrt{1+2 \, \left(\frac{CV}{100}\right) ^2}$$
where ##SE## is the standard error, ##CV## is the coefficient of variation and ##n## is the number of observations.
However, I don't know if this formula holds for the exponential distribution.
 
Greetings, I am studying probability theory [non-measure theory] from a textbook. I stumbled to the topic stating that Cauchy Distribution has no moments. It was not proved, and I tried working it via direct calculation of the improper integral of E[X^n] for the case n=1. Anyhow, I wanted to generalize this without success. I stumbled upon this thread here: https://www.physicsforums.com/threads/how-to-prove-the-cauchy-distribution-has-no-moments.992416/ I really enjoyed the proof...

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