Relating the CDF to the probability density

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Homework Help Overview

The discussion revolves around proving a relationship involving the expected value of a random variable defined on the interval [0, ∞] and its cumulative distribution function (CDF). The original poster presents a specific integral expression for the expected value, prompting questions about the limits of integration and the behavior of the CDF.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants explore the validity of the proposed limits of integration for the expected value, with some questioning the independence of the expected value from the behavior of the CDF in the interval [0, 1]. Others suggest that the correct formulation should involve integration from 0 to ∞.

Discussion Status

There is an ongoing examination of the assumptions underlying the original statement, with some participants providing alternative formulations and suggesting methods such as integration by parts. The discussion reflects a mix of interpretations regarding the conditions under which the expected value can be calculated.

Contextual Notes

Participants note that the proof may depend on whether the CDF is absolutely continuous and the implications of this on the existence of a density function. There is also mention of the complexity involved if the CDF does not have a density function.

bonfire09
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Homework Statement


If ##X## is any random variable defined on ##[0,\infty]## with continuous CDF ##F_X(t)##. Prove that ##E(X)=\int_1^\infty (1-F_X(t)) dt##.
.

Homework Equations


The Attempt at a Solution


I am not sure how to go about this. I think double integration can be used to prove it, but I don't want to go down that path. Is there another which I can prove this. Thanks.
 
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Are you sure about those limits? I don't see how E(X) can be independent of the behaviour of F between 0 and 1.
 
Well it says that ##X## is a random variable with range ##[0, \infty]##.
 
bonfire09 said:

Homework Statement


If ##X## is any random variable defined on ##[0,\infty]## with continuous CDF ##F_X(t)##. Prove that ##E(X)=\int_1^\infty (1-F_X(t)) dt##.
.

Homework Equations





The Attempt at a Solution


I am not sure how to go about this. I think double integration can be used to prove it, but I don't want to go down that path. Is there another which I can prove this. Thanks.

Somebody has asked you to prove a false result. The correct version is
EX = \int_0^{\infty} [1 - F_X(x)] \,dx .

Exactly how you should prove it depends on what you know about Lebesgue-Stieltjes integrals, or whether you even need to use them. The proof is easiest if you assume that ##F_X(\cdot)## is absolutely continuous, in which case there is a density ##f_X(\cdot)## on ##(0,\infty)## giving ##F_X(x) = \int_0^x f_X(t) \, dt## for all ##x > 0##. If ##F_X## is not absolutely continuous, it can be continuous but not have a density function; then the proof is harder.

I suggest you try it first for the case where a density function exists. Think integration by parts.
 
Last edited:
bonfire09 said:

Homework Statement


If ##X## is any random variable defined on ##[0,\infty]## with continuous CDF ##F_X(t)##. Prove that ##E(X)=\int_1^\infty (1-F_X(t)) dt##.
.


haruspex said:
Are you sure about those limits? I don't see how E(X) can be independent of the behaviour of F between 0 and 1.

I agree; it should be E(X) = \int_0^\infty 1 - F_X(t)\,dt.

Homework Equations





The Attempt at a Solution


I am not sure how to go about this. I think double integration can be used to prove it, but I don't want to go down that path. Is there another which I can prove this. Thanks.

This is an application of integration by parts. You want to calculate <br /> E(X) = \int_0^\infty xF_X&#039;(x) \,dx. Note that <br /> F_X&#039;(x) = -\frac{d}{dx}(1 - F_X(x)).<br />
 

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