How to prove the Cauchy distribution has no moments?

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SUMMARY

The Cauchy distribution has no moments for all integers n due to the divergence of the integral E(X^n) = ∫_{-∞}^∞ (x^n / (π(1+x^2))) dx. The proof involves demonstrating that E[|X|] = ∞, which implies E[|X|^n] = ∞ for all n. The discussion highlights the relationship between the geometric mean (GM) and arithmetic mean (AM) inequalities, which are utilized to establish the non-existence of moments. The integrand diverges as |x| approaches infinity for n > 2, confirming that the moments do not exist.

PREREQUISITES
  • Understanding of probability distributions, specifically the Cauchy distribution.
  • Familiarity with integral calculus and improper integrals.
  • Knowledge of mathematical expectations and the Law of the Unconscious Statistician (LOTUS).
  • Basic concepts of inequalities, particularly the geometric mean (GM) and arithmetic mean (AM).
NEXT STEPS
  • Study the properties of the Cauchy distribution in detail, focusing on its lack of moments.
  • Learn about improper integrals and their convergence criteria.
  • Explore the Law of the Unconscious Statistician (LOTUS) in the context of expectations.
  • Investigate the applications of GM and AM inequalities in probability theory.
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Mathematicians, statisticians, and students studying probability theory who are interested in the properties of distributions and the concept of moments.

Neothilic
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TL;DR
Proving how the Cauchy distribution has no moments for all n.
How can I prove the Cauchy distribution has no moments?

##E(X^n)=\int_{-\infty}^\infty\frac{x^n}{\pi(1+x^2)}\ dx.##

I can prove myself, letting ##n=1## or ##n=2## that it does not have any moment. However, how would I prove for ALL ##n##, that the Cauchy distribution has no moments?
 
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Sorry for the dumb I'm not a mathematician question but why isn't

## E_A(X^n) = \int_{-A}^A \frac{x^n}{\pi(1+x^2)}dx = 0 ##

for all ##n## odd and all ##A##?
 
so you've proven
##E\Big[\big \vert X\big \vert\Big] = \infty##

in general
##\big \vert X\big \vert= \Big(\big \vert X\big \vert^n \cdot 1\Big)^\frac{1}{n}= \Big(\big \vert X\big \vert^n \cdot \prod_{k=1}^{n-1}1\Big)^\frac{1}{n} \leq \frac{1}{n} \Big(\big \vert X\big \vert^n + \sum_{k=1}^{n-1}1 \Big) =\frac{1}{n} \big \vert X\big \vert^n + \frac{n-1}{n}##
by ##\text{GM}\leq \text{AM}##

Now take expectations of each side (and use LOTUS)
##E\Big[\big \vert X\big \vert\Big]\leq \frac{1}{n} E\Big[\big \vert X\big \vert^n\Big] + \frac{n-1}{n}##

thus
##E\Big[\big \vert X\big \vert\Big] = \infty \longrightarrow E\Big[\big \vert X\big \vert^n\Big] = \infty##
 
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StoneTemplePython said:
so you've proven
##E\Big[\big \vert X\big \vert\Big] = \infty##

in general
##\big \vert X\big \vert= \Big(\big \vert X\big \vert^n \cdot 1\Big)^\frac{1}{n}= \Big(\big \vert X\big \vert^n \cdot \prod_{k=1}^{n-1}1\Big)^\frac{1}{n} \leq \frac{1}{n} \Big(\big \vert X\big \vert^n + \sum_{k=1}^{n-1}1 \Big) =\frac{1}{n} \big \vert X\big \vert^n + \frac{n-1}{n}##
by ##\text{GM}\leq \text{AM}##

Now take expectations of each side (and use LOTUS)
##E\Big[\big \vert X\big \vert\Big]\leq \frac{1}{n} E\Big[\big \vert X\big \vert^n\Big] + \frac{n-1}{n}##

thus
##E\Big[\big \vert X\big \vert\Big] = \infty \longrightarrow E\Big[\big \vert X\big \vert^n\Big] = \infty##

Wow. I have been trying to prove this for a couple weeks now. I have tried doing series test with the integrands etc. However, this is so unique and short and brilliant! How did you come up with this? Btw, what is meant by GM and AM?
 
##\frac{|x^n|}{1+x^2}\to \infty## as ##|x|\to \infty##, for ##n\gt 2##. So the integral blows up.
 
mathman said:
##\frac{|x^n|}{1+x^2}\to \infty## as ##|x|\to \infty##, for ##n\gt 2##. So the integral blows up.
And even for n=2 the integrand does not go to zero, that means all these cases can be solved without calculating anything. Only n=1 needs some thought.
Neothilic said:
Btw, what is meant by GM and AM?
Geometric mean, arithmetic mean

You opened three threads for basically the same topic - it would have been better to keep the discussion in one thread. But now merging them would create a mess so I keep them separate.

https://www.physicsforums.com/threa...nts-using-the-characteristic-function.992462/
https://www.physicsforums.com/threads/what-is-the-nth-differential-of-this-equation.992492/
 
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