MHB Is the Sum of Exponential Random Variables a Gamma Distribution?

[Chris L T521]

Thanks again to those that participated in the second round of our POTW! Now, it's time for the third one! (Bigsmile)

This week's problem was proposed by yours truly.

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Problem: Let $X_i,\, (i=1,\ldots,n)$ be a (continuous) random variable of the exponential distribution $\text{Exp}(\lambda)$, where it's probability density function (p.d.f.) is defined by

\[f(x) = \left\{\begin{array}{cl}\lambda e^{-\lambda x} & x\geq 0,\,\lambda >0\\ 0 & x<0\end{array}\right.\]

Show that $\sum_{i=1}^n X_i$ is equivalent to a random variable of the Gamma distribution $\Gamma(n,\theta)$, where the p.d.f. of the Gamma distribution is given by

\[f(x) = \left\{\begin{array}{cl}\frac{1}{\theta^n\Gamma(n)}x^{n-1}e^{-x/\theta} & x\geq 0,\,\theta>0,\, n\in\mathbb{Z}^+\\ 0 & x<0\end{array}\right.\]

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Here are two hints:

Spoiler

If $X$ is a continuous random variable, we define the moment generating function by

\[M_X(t) = E[e^{tX}] = \int_{-\infty}^{\infty}e^{tx}f(x)\,dx\]

where $f(x)$ is the p.d.f. of the random variable $X$. Use the fact that if $\{X_i\}_{i=1}^n$ is a collection of random variables, then

\[M_{\sum_{i=1}^n X_i}(t) = \prod_{i=1}^n M_{X_i}(t)\]

Spoiler

Recall that $\Gamma(x) = \displaystyle\int_0^{\infty}e^{-t}t^{x-1}\,dx$.

Remember to read the http://www.mathhelpboards.com/showthread.php?772-Problem-of-the-Week-(POTW)-Procedure-and-Guidelines to find out how to http://www.mathhelpboards.com/forms.php?do=form&fid=2!

EDIT: I forgot to mention that each $X_i$ are i.i.d. random variables. If they're not, then the above result doesn't hold (thanks to girdav for pointing this out).

 

[Chris L T521]

Sadly, no one answered the question gave a correct solution this week!

Here's the solution to the problem.

Spoiler

By a simple calculation, we can show that if $X_i\sim \text{Exp}(\lambda)$, then

\[\begin{aligned}M_{X_i}(t) &= \int_0^{\infty}e^{tx}\lambda e^{-\lambda x}\,dx\\ &= \lambda\int_0^{\infty}e^{-(\lambda-t)x}\,dx\\ &= \frac{\lambda}{\lambda-t}\int_0^{\infty}e^{-u}\,du\,\quad \text{(by making the substitution $u=(\lambda-t)x$}\\ &= \frac{\lambda}{\lambda-t}=\frac{1}{1-(t/\lambda)}\end{aligned}\]

Let $X\sim\Gamma(n,\theta)$. Then

\[\begin{aligned}M_X(t) &= \frac{1}{\theta^n\Gamma(n)}\int_0^{\infty}x^{n-1}\exp\left(-\left(\frac{1}{\theta}-t\right)x\right)\,dx\\
&= \frac{1}{\theta^n\Gamma(n)} \int_0^{\infty}\frac{1}{(1/\theta - t)^n}u^{n-1}e^{-u}\,du\quad\text{(use the substitution $u=(1/\theta - t)x$}\\ &= \frac{1}{\theta^n\Gamma(n)(1/\theta- t)^n}\Gamma(n)\quad\text{(by definition of the Gamma function)}\\ &=\frac{1}{\theta^n(1/\theta - t)^n}=\frac{1}{(1-t\theta)^n}.\end{aligned}\]

Thus,

\[\begin{aligned}M_{\sum_{i=1}^n X_i}(t) &= \prod_{i=1}^n M_{X_i}(t)\\ &= \prod_{i=1}^n \frac{1}{1 - (t/\lambda)}\\ &= \frac{1}{(1-(t/\lambda))^n}\end{aligned}\]

If we make the substitution $\frac{1}{\theta}=\lambda$, then we see that

\[M_{\sum_{i=1}^n X_i}(t) = \frac{1}{(1-t\theta)^n} = M_X(t)\]

where $X\sim\Gamma(n,\theta)$.

Therefore, $X=\sum_{i=1}^n X_i.\qquad\blacksquare$