Calculation of E[X|X>Y] for Exponential Random Variables

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Discussion Overview

The discussion revolves around the calculation of the conditional expectation E[X|X>Y] for exponential random variables X and Y. Participants explore the mathematical formulation and integration techniques required to derive this expectation, focusing on joint distributions and the appropriate limits for integration.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant expresses difficulty in building the integral structure for E[X|X>Y] and suggests that the result might be 1/μ, but seeks proof.
  • Another participant advises starting with the double integral for the unconditional expectation E[X] and adjusting the inner limits to derive the conditional expectation E[X|X>Y].
  • A different participant discusses their approach involving E(X1|X1
  • One participant challenges the use of a single integral instead of a double integral, emphasizing the need for the latter in this context.
  • Another participant reiterates the need to find E[X|Y, X>Y] and suggests averaging over all values of Y after determining the conditional expectation.

Areas of Agreement / Disagreement

Participants do not appear to reach a consensus on the correct approach to calculating E[X|X>Y]. There are multiple competing views on how to structure the integrals and the necessity of using double integrals.

Contextual Notes

Some participants question the independence of X and Y, which may affect the calculations. There are also unresolved aspects regarding the integration limits and the correctness of individual approaches presented.

mertcan
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Hi, Initially X and Y are exponential random variables with rate respectively $$\mu \lambda$$, and I am aware that E[X|X>Y] is obtained using joint distribution but I can not build up the integral structure, I intuitively think the result is just 1/mu, but I can not prove it to myself could you help me about that and building the integral structure?
 
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Start by writing the double integral that calculates the unconditional expectation ##E[X]##. Use ##x## as integration variable for the outer integral, and ##y## for the inner integral. Once you've done that, only a minor adjustment is needed to the inner integration limits to turn it into the conditional expectation ##E]X|X>Y]##.
 
hi, I tried to do my work related to E(X1|X1<X2)*P(X1<X2), and X1, X2 are exponential random variables with rate respectively $$\lambda, \mu$$ I found a answer but I think it is wrong so could you tell me which part of my work is wrong?? ( I also looking forward to your answers @andrewkirk @Ray Vickson :) )
 

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Did you read my post? I told you that you need a double integral. Why have you tried to do something using only a single integral? The image you posted is too dark and smudgy to make out in detail what it says but even at a glance one can see that it only has single integrals, not double integrals.
 
@andrewkirk @Ray Vickson I upload my work 2 ,let me express again that E(X1|X1<X2)*P(X1<X2), and X1, X2 are exponential random variables with rate respectively $$\lambda_1,\lambda_2$$I found a answer but I think it is wrong so could you tell me which part of my work is wrong?? ( by the way I did my best to make it not dark, when I upload, the top and bottom parts get dark a little )
 

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mertcan said:
Hi, Initially X and Y are exponential random variables with rate respectively $$\mu \lambda$$, and I am aware that E[X|X>Y] is obtained using joint distribution but I can not build up the integral structure, I intuitively think the result is just 1/mu, but I can not prove it to myself could you help me about that and building the integral structure?

Are X and Y independent? In any case, you first need to find $$\mathbb{E}[X|Y, X>Y]$$. To do this, first write $$\mathbb{E}[X]$$, and then change the lower limit. After finding $$\mathbb{E}[X|Y, X>Y]$$, you will need to average over all values of Y. If you go through these steps, you should be able to find what you want.
 

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