What is the Expectation of (aX-bY)?

In summary, the conversation is about finding the integral form for E[a/X-b/Y] and using the distribution functions of nonnegative Gamma distributed random variables to evaluate it. The first case, aX - bY, is easy to calculate, while the second case, a/X - b/Y, requires finding the distribution function for the reciprocals and using the addition formula. The conversation also discusses the possibility of splitting the expectation and using the inverse-gamma distribution to find E(1/X). However, it is suggested to use the original Gamma distribution and the Stieljes integral formula to evaluate E(1/X).
  • #1
nikozm
54
0
Hello,

I 'm trying to express the following in integral form:

E[a/X-b/Y], where E[.] stands for the expectation operator.

Let a,b be some nonnegative constants and X,Y are independent nonnegative Gamma distributed random variables.

Any help would be useful.

Thanks in advance
 
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  • #2
Do you want aX - bY or a/X - b/Y? The first is easy.
For the second you need to first get the distribution functions for the reciprocals. Then use the addition formula.
 
  • #3
Thanks for the reply.

i 'm interested in evaluating the second on (i.e., a/X - b/Y). Can you be more specific ? the addition formula with what integration bounds exactly?
what if i would use the expectation of X and Y seperately (i.e, E[c/X]-E[b/Y])?
 
  • #4
Splitting the expectation by linearity is the obvious first step. The hard question is given a distribution X, find E(1/X). This is non-trivial in general, but luckily for you for a gamma distribution this has already been done

http://en.wikipedia.org/wiki/Inverse-gamma_distribution
 
  • #5
This a well-known result for resiprocal PDFs. In fact, this is a generic rule for every distribution function (i.e. taking the inverse variable within the initial PDF and multiplying it with the derivative of the inverse variable gives the corresponding reciprocal PDF).

I was hoping for a solution by applying the original Gamma PDFs directly..
 
  • #6
Let X be a random variable with density function f(x), then E(1/X) = ∫(f(x)/x)dx.
If there is no density and the distribution function is F(x), you need a Stieljes integral, E(1/X) = ∫(1/x)dF(x)
 

1. What is the formula for calculating the expectation of (aX-bY)?

The formula for calculating the expectation of (aX-bY) is E(aX-bY) = aE(X) - bE(Y), where E(X) and E(Y) are the expectations of X and Y respectively.

2. What does the expectation of (aX-bY) represent?

The expectation of (aX-bY) represents the average value that we would expect to obtain from the random variables aX and bY when they are combined in the given way.

3. How does the expectation of (aX-bY) change if a and b are constants?

If a and b are constants, then the expectation of (aX-bY) will also be a constant, regardless of the values of X and Y. This is because the expectation operator is a linear operator, meaning that it follows the properties of linearity.

4. Can the expectation of (aX-bY) be negative?

Yes, the expectation of (aX-bY) can be negative. This can happen if the random variables X and Y have different signs, or if the constants a and b are chosen in a way that results in a negative expectation.

5. How is the expectation of (aX-bY) related to the variance?

The expectation of (aX-bY) and the variance are not directly related. However, the variance of aX-bY can be calculated using the formula Var(aX-bY) = a²Var(X) + b²Var(Y) - 2abCov(X,Y), where Var(X) and Var(Y) are the variances of X and Y respectively, and Cov(X,Y) is their covariance.

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