Expected value of g(x)=1/(x+c) under gamma distribution

It is interesting that the first and second moments for f(x) = 1/(1+cx) are given by the same series formula:m1 = c^2 + c^3 + c^4 + ...= c^2/(1-c)m2 = c^3 + 2c^4 + 3c^5 + ...= c^3/(1-c)^2.I suspect that if we find the formula for m3 and m4, we may be able to find the closed form solution for \int_0^{\infty} \frac{1}{x+c} \frac{1}{\Gamma(\alpha) \beta^\alpha} x^{\alpha \,-\,
  • #1
PMRDK
3
0

Homework Statement



Let [itex] g(x) = \frac{1}{x+c}[/itex], where [itex]c[/itex] is a positive constant, and [itex]x [/itex] is a random variable distributed according to the Gamma distribution
[itex]x\sim f(x)=\frac{1}{\Gamma(\alpha) \beta^\alpha} x^{\alpha \,-\, 1} e^{-\frac{x}{\beta}}[/itex].

I wish to calculate the expected value of [itex]g(x)[/itex] with respect to the probability density function [itex]f(x)[/itex].

Homework Equations



The expected value can be calculated as
[tex]E(g(x))=\int_0^∞g(x)f(x)dx = \int_0^∞ \frac{1}{x+c} \frac{1}{\Gamma(\alpha) \beta^\alpha} x^{\alpha \,-\, 1} e^{-\frac{x}{\beta}}dx
[/tex]


The Attempt at a Solution


I have problems with calculating the integral. If [itex]g(x)=\frac{1}{x}[/itex], then the integral would not be too difficult. But the constant in the denominator gives me problems. I have attempted with variable substitutions and integration by parts. However, I have not been able to come up with a solution.
Actually, this is not a homework problem. I posted it here since it is `homework style', so I do not know if it is event possible to calculate the expectation.

Any help is much appreciated.
 
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  • #2
The integral may not have a closed form. Do you know for sure if it does?

I'm not sure if this will help much, but you could try splitting up the integral into two, one from ##[0,c)## and the other on ##[c,\infty)##, and then expand the 1/(x+c) as a power series and integrate term by term to get a series expression. In the first integral, since x is smaller than c, you would use

$$\frac{1}{x+c} = \frac{1}{c} \frac{1}{1+x/c} = \frac{1}{c} \sum_{n=0}^\infty \left(\frac{x}{c}\right)^n,$$

while in the second term x is greater than c, so you would write

$$\frac{1}{x+c} = \frac{1}{x} \frac{1}{1+c/x} = \frac{1}{x} \sum_{n=0}^\infty \left(\frac{c}{x}\right)^n.$$

In principle one needs to be careful about switching the integral and the sum since the geometric series is only uniformly convergent on a domain ##|z-c| \leq R## for R < 1, so if you want to be rigorous I guess you could split up the integral into three regions, ##[0,c-\epsilon), [c-\epsilon,c+\epsilon], (c+\epsilon,\infty)## and do the series expansion for the first and the last, then estimate the error from neglecting the ##[c-\epsilon,c+\epsilon]## integral when taking ##\epsilon \rightarrow 0##, but my guess is that it won't cause problems.
 
  • #3
PMRDK said:

Homework Statement



Let [itex] g(x) = \frac{1}{x+c}[/itex], where [itex]c[/itex] is a positive constant, and [itex]x [/itex] is a random variable distributed according to the Gamma distribution
[itex]x\sim f(x)=\frac{1}{\Gamma(\alpha) \beta^\alpha} x^{\alpha \,-\, 1} e^{-\frac{x}{\beta}}[/itex].

I wish to calculate the expected value of [itex]g(x)[/itex] with respect to the probability density function [itex]f(x)[/itex].

Homework Equations



The expected value can be calculated as
[tex]E(g(x))=\int_0^∞g(x)f(x)dx = \int_0^∞ \frac{1}{x+c} \frac{1}{\Gamma(\alpha) \beta^\alpha} x^{\alpha \,-\, 1} e^{-\frac{x}{\beta}}dx
[/tex]


The Attempt at a Solution


I have problems with calculating the integral. If [itex]g(x)=\frac{1}{x}[/itex], then the integral would not be too difficult. But the constant in the denominator gives me problems. I have attempted with variable substitutions and integration by parts. However, I have not been able to come up with a solution.
Actually, this is not a homework problem. I posted it here since it is `homework style', so I do not know if it is event possible to calculate the expectation.

Any help is much appreciated.

Using b instead of 1/β, Maple evaluates this integral in terms of the incomplete Gamma function:

f:=b^a*x^(a-1)/GAMMA(a)*exp(-b*x);
f = b^a*x^(a-1)/GAMMA(a)*exp(-b*x)

J1:=int(f/(x+c),x=0..infinity) assuming a>0,b>0,c>0;
J1 = b^a*c^(a-1)*exp(c*b)*GAMMA(1-a,c*b)

That is,
[tex] \int_0^{\infty} \frac{b^a}{\Gamma(a)} \frac{x^{a-1}}{x+c} e^{-bx} \, dx
= b^a c^{a-1} e^{bc} \Gamma(1-a,bc),[/tex]
where
[tex] \Gamma(u,z) = \int_z^{\infty} e^{-t} t^{u-1} \, dt \text{ if } z > 0[/tex]
is the incomplete Gamma function. I believe it has been proven that the incomplete Gamma function is non-elementary if u is not a positive integer.
 
Last edited:
  • #4
Thank you for your help:smile:. So Maple was able to provide an answer in terms of a closed form solution (I tried in Maxima but did not succeed).
 
  • #5
A more general problem has been solved here, for [itex]1/(1+cx^n)[/itex] and [itex]1/(1+cx)^n[/itex], for any [itex]n[/itex]:
http://www.pnas.org/content/107/51/22096.full.pdf+html?with-ds=yes

You can find there the explicit formula for the probability density function, the monotonicity properties of the pdf, the first and second moments for [itex]n=1[/itex] and [itex]n=2[/itex].
 

1. What is the expected value of g(x) under gamma distribution?

The expected value of g(x) under gamma distribution is the sum of all possible values of g(x) multiplied by their respective probabilities. It is often denoted as E[g(x)] or μ.

2. How is the expected value of g(x) calculated under gamma distribution?

The expected value of g(x) under gamma distribution is calculated by integrating g(x) times the probability density function of the gamma distribution, which is given by f(x) = (1/Γ(c)) * x^(c-1) * e^(-x) where c is the shape parameter and x is the random variable.

3. Can the expected value of g(x) be negative under gamma distribution?

Yes, the expected value of g(x) under gamma distribution can be negative if the shape parameter c is less than 1. In this case, the function g(x) may take negative values and therefore, the expected value will also be negative.

4. How does the shape parameter affect the expected value of g(x) under gamma distribution?

The shape parameter c has a significant impact on the expected value of g(x) under gamma distribution. As c increases, the expected value also increases and approaches infinity. On the other hand, as c decreases, the expected value decreases and approaches zero.

5. Can the expected value of g(x) be greater than 1 under gamma distribution?

Yes, the expected value of g(x) can be greater than 1 under gamma distribution. This can happen when the shape parameter c is less than 1, as the function g(x) may take values greater than 1 and therefore, the expected value will also be greater than 1.

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