Is the Finite Expectation of Powers Satisfied by Nonnegative Random Variables?

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Discussion Overview

The discussion centers around the conditions under which the finite expectation of powers of a nonnegative random variable is satisfied. Participants explore the relationship between the expectation of powers of the random variable and a summation involving its probabilities, with a focus on the implications of the random variable not having a density.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant proposes that for a nonnegative random variable ##X## and ##p \in (0, \infty)##, the condition ##\mathbb{E}[X^p] < \infty## is equivalent to the summation ##\sum_{n = 1}^\infty n^{p-1}P(X \ge n) < \infty##.
  • Another participant expresses that demonstrating the equivalence is the challenging aspect of the problem.
  • A participant notes that the problem does not assume that ##X## has a density, which may affect the approach taken.
  • A further reply suggests a specific representation of the random variable when it takes positive integer values, indicating that a series representation could coincide with an integral in this context.

Areas of Agreement / Disagreement

Participants have not reached a consensus on the equivalence of the conditions discussed, and there are varying perspectives on how to approach the problem, particularly regarding the assumptions about the random variable.

Contextual Notes

The discussion highlights the lack of assumptions regarding the density of the random variable, which may influence the validity of certain approaches and representations proposed by participants.

Euge
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Suppose ##X## is a nonnegative random variable and ##p\in (0,\infty)##. Show that ##\mathbb{E}[X^p] < \infty## if and only if ##\sum_{n = 1}^\infty n^{p-1}P(X \ge n) < \infty##.
 
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\mathbb{E}[X^p] =\int_0^\infty x^p p(x) dx = [ x^p \sigma(x)]_0^\infty - p \int_0^{\infty} x^{p-1}\sigma(x) dx
= p \int_0^{\infty} x^{p-1}(1-\sigma(x) )dx
where
\sigma(x)=\int_0^x p(t) dt,\ \ \sigma(\infty)=1
The integral differs from the given series by finite quantity. They both are finite or infinite.
 
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anuttarasammyak said:
\mathbb{E}[X^p] =\int_0^\infty x^p p(x) dx = [ x^p \sigma(x)]_0^\infty - p \int_0^{\infty} x^{p-1}\sigma(x) dx
= p \int_0^{\infty} x^{p-1}(1-\sigma(x) )dx
where
\sigma(x)=\int_0^x p(t) dt,\ \ \sigma(\infty)=1
The integral differs from the given series by finite quantity. They both are finite or infinite.
I think the tricky part of this problem is showing your second to last sentence. A non-answer spoiler:
Euler-Maclaurin with one of the limits going to infinity has to be treated carefully
 
Hi @anuttarasammyak, in this problem ##X## is not assumed to have a density.
 
Euge said:
Hi @anuttarasammyak, in this problem ##X## is not assumed to have a density.
I see. If X is a positive integer I hope the replacement
p(x)=\sum_{n=1}^{\infty}\ P_n\ \delta(x-n)
where n is positive integer and
\sum_{n=1}^{\infty}P_n=1,0 \leq P_n
would work in my previous post. The integral and the series coincide.
 
Last edited:

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