Conditional expectation of three exponential distributed r.v.

In summary: If you make a change of variables to a=x, b=x+y, c=x+y+z, the integral becomes\int_\Delta \int_0^\infty (x+y) f_A(x)(x+y+z)f_B(x+y)f_C(x+y+z)dydxwhere \Delta is the region 0<x<y<z. This is equal to\int_0^\infty \int_0^\infty \int_0^\infty (x+y) f_A(x)(x+y+z)f_B(x+y)f_C(x+y+z)dzdydxby Fubini's Theorem. This is then equal to\int_0^\infty \int_
  • #1
Ego7894612
4
0
I've been struggling with this problem for more than 4 days now:

Let A, B and C be exponential distributed random variables with parameters lambda_A, lambda_B and lambda_C, respectively.

Calculate E [ B | A < B < C ] in terms of the lambda's.

I always seem get an integral which is impossible to calculate...

Who has a suggestion how to solve this problem?
 
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  • #2
Ego7894612 said:
I always seem get an integral which is impossible to calculate...

That's how I'd try to do it too, can you show us what you got and where you were stuck?
 
  • #3
First a remark: we may assume that A, B and C are independent random variables (this may help a lot).

[tex]
E [ B | A < B < C ] = \int_{0}^\infty E [ B \, | \, A < B < C, A = a ] \; f_{A} ( a ) \; da
[/tex]
[tex]
= \int_{0}^\infty \int_{0}^\infty E [ B \, | \, A < B < C, A = a, C=c ] \; f_{A} ( a ) \; f_{C} ( c ) \; da \; dc.
[/tex]
(Along with all that follows, this integral is only to be considered over R_+^2 and for c>a.)
[tex]
= \int_{0}^\infty \int_{0}^\infty E [ B \, | \, a < B < c, A = a, C=c ] \; f_{A} ( a ) \; f_{C} ( c ) \; da \; dc
[/tex]
(by independence)
[tex]
= \int_{0}^\infty \int_{0}^\infty E [ B \, | \, a < B < c ] \; f_{A} ( a ) \; f_{C} ( c ) \; da \; dc
[/tex]

We want to compute [tex] E [ B \, | \, a < B < c ]. [/tex]

First note that
[tex] E [ B \, | \, a < B < c ] = \frac{ \int_a^c t f_{B} (t) dt }{P( B \in [a,c])}.[/tex]

We have[tex] \int_a^c t f_{B} (t) dt = ... = (a e^{- \lambda_B a} - c e^{- \lambda_B c}) + \frac{1}{\lambda_B} ( e^{- \lambda_B a} - e^{- \lambda_B c} ) [/tex]
and
[tex] P(B \in [a,c]) = F_B (c) - F_B (a) = ... = e^{- \lambda_B a} - e^{- \lambda_B c}. [/tex]

Hence
[tex] E [ B \, | \, a < B < c ] = \frac{ (a e^{- \lambda_B a} - c e^{- \lambda_B c}) + \frac{1}{\lambda_B} ( e^{- \lambda_B a} - e^{- \lambda_B c} ) }{e^{- \lambda_B a} - e^{- \lambda_B c}},[/tex]
[tex] E [ B \, | \, a < B < c ] = \frac{ a e^{- \lambda_B a} - c e^{- \lambda_B c} }{e^{- \lambda_B a} - e^{- \lambda_B c}} + \frac{1}{\lambda_B}.[/tex]

So
[tex]
E [ B | A < B < C ] = \int_{0}^\infty \int_{0}^\infty \left( \frac{ a e^{- \lambda_B a} - c e^{- \lambda_B c} }{e^{- \lambda_B a} - e^{- \lambda_B c}} + \frac{1}{\lambda_B} \right) \; f_{A} ( a ) \; f_{C} ( c ) \; da \; dc
[/tex]
[tex]
= \int_{0}^\infty \int_{0}^\infty \left( \frac{ a e^{- \lambda_B a} - c e^{- \lambda_B c} }{e^{- \lambda_B a} - e^{- \lambda_B c}} \right) \lambda_A e^{- \lambda_A a} \; \lambda_C e^{- \lambda_C c} \; da \; dc +
[/tex]
[tex]
\int_{0}^\infty \int_{0}^\infty \frac{1}{\lambda_B} \lambda_A e^{- \lambda_A a} \; \lambda_C e^{- \lambda_C c} \; da \; dc
[/tex]Here's my problem: I can't compute the first of these integrals... And even then I'm not sure if what I'm doing here is correct... Is my calculation right, or is it wrong!? Is there perhaps a better way to calculate this? Etc.
 
Last edited:
  • #4
Ego7894612 said:
[tex]
E [ B | A < B < C ] = \int_{0}^\infty E [ B \, | \, A < B < C, A = a ] \; f_{A} ( a ) \; da
[/tex]

I'm not sure about this line - the marginals of the conditional distribution aren't necessarily exponential. Another approach is to apply directly the definition of conditional probabilities, so that

[tex]E[B|A<B<C] = E[B.I(A<B<C)]/P[A<B<C][/tex]

which is a ratio of integrals that should both be tractable.

Hope this helps.
 
  • #5
bpet said:
I'm not sure about this line - the marginals of the conditional distribution aren't necessarily exponential. Another approach is to apply directly the definition of conditional probabilities, so that

[tex]E[B|A<B<C] = E[B.I(A<B<C)]/P[A<B<C][/tex]

which is a ratio of integrals that should both be tractable.

Hope this helps.

Isn't this just the "law of total expectation"?

I don't really understand your point I'm afraid.

EDIT: Yes, we can also go by the second approach.

Then how to calculate [tex] E[B.I(A<B<C)] [/tex] exactly ? (I'm not sure how to start with this integral.)
 
  • #6
Ego7894612 said:
Isn't this just the "law of total expectation"?

I don't really understand your point I'm afraid.

EDIT: Yes, we can also go by the second approach.

Then how to calculate [tex] E[B.I(A<B<C)] [/tex] exactly ? (I'm not sure how to start with this integral.)

I now understand what you mean by "I'm not sure about this line (...)": you're completely right, it's wrong!
 
  • #7
Ego7894612 said:
Then how to calculate [tex] E[B.I(A<B<C)] [/tex] exactly ? (I'm not sure how to start with this integral.)

Ok, this can be simplified to

[tex]\int_R b.f_A(a).f_B(b).f_C(c)da.db.dc[/tex]

where R is the region a<b<c.
 

Related to Conditional expectation of three exponential distributed r.v.

1. What is conditional expectation?

Conditional expectation is a statistical concept that represents the average value of a random variable given certain information or conditions. It is denoted as E[X|Y] and is calculated by taking the product of the probability of each possible value of X with its corresponding value of Y, and then summing all of these products together.

2. How is conditional expectation calculated for three exponential distributed random variables?

The conditional expectation for three exponential distributed random variables is calculated by using the formula E[X|Y,Z] = E[X] + E[Y] + E[Z], where E[X], E[Y], and E[Z] are the individual expected values of each random variable. This formula assumes that the three random variables are independent of each other.

3. Can conditional expectation be negative?

Yes, conditional expectation can be negative. This can happen if the random variable has a higher probability of taking on lower values. In this case, the average value would be negative. It is important to note that conditional expectation is an average value and can take on any real number, including negative values.

4. How is conditional expectation used in statistical analysis?

Conditional expectation is used in statistical analysis to calculate the expected value of a random variable given certain conditions. It can help to predict the outcome of an experiment or study by taking into account additional information or variables. It is also used in regression analysis to determine the relationship between two or more variables.

5. What is the relationship between conditional expectation and conditional probability?

Conditional expectation and conditional probability are closely related. Conditional probability is the likelihood of an event occurring given certain conditions, while conditional expectation is the average value of a random variable given those same conditions. In other words, conditional expectation is the expected value of the conditional probability distribution.

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