How to prove the Cauchy distribution has no moments?

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Discussion Overview

The discussion revolves around proving that the Cauchy distribution has no moments, specifically exploring the mathematical reasoning behind this assertion. Participants examine various approaches to demonstrate the lack of moments for all orders of n, including integrals and properties of expectations.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant presents the integral for the nth moment of the Cauchy distribution and seeks a general proof for all n.
  • Another participant questions why the integral of the Cauchy distribution over a finite range yields zero for odd n, suggesting a misunderstanding of the behavior of the integrand.
  • Several participants discuss the implications of the expectation of the absolute value of X being infinite, leading to the conclusion that the moments must also be infinite.
  • One participant expresses admiration for a proof approach involving the geometric mean and arithmetic mean inequality, indicating a breakthrough in understanding.
  • Another participant notes that for n greater than 2, the integrand approaches infinity as x approaches infinity, leading to the conclusion that the integral diverges.
  • There is a clarification regarding the terms "GM" and "AM," with participants confirming they refer to geometric mean and arithmetic mean, respectively.

Areas of Agreement / Disagreement

Participants generally agree on the assertion that the Cauchy distribution does not have moments, but the discussion includes various methods and interpretations of the proof, indicating that multiple approaches are being explored without a single consensus on the most effective method.

Contextual Notes

Some participants express uncertainty about specific steps in the proofs and the behavior of the integrand for different values of n, highlighting the complexity of the topic and the need for careful consideration of mathematical properties.

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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