(New page: =Example. Sequence of binomially distributed random variables= Let <math>\left\{ \mathbf{X}_{n}\right\} _{n\geq1}</math> be a sequence of binomially distributed random variables, with th...)
 
Line 35: Line 35:
  
 
The problem 2 of the August 2007 QE [CS1QE2007August] is identical to this example.
 
The problem 2 of the August 2007 QE [CS1QE2007August] is identical to this example.
 +
 +
----
 +
[[ECE600|Back to ECE600]]
 +
 +
[[ECE 600 Exams|Back to ECE 600 Exams]]

Revision as of 13:12, 23 November 2010

Example. Sequence of binomially distributed random variables

Let $ \left\{ \mathbf{X}_{n}\right\} _{n\geq1} $ be a sequence of binomially distributed random variables, with the $ n_{th} $ random variable $ \mathbf{X}_{n} $ having pmf

$ P_{\mathbf{X}_{n}}(k)=P\left(\left\{ \mathbf{X}_{n}=k\right\} \right)=\left(\begin{array}{c} n\\ k \end{array}\right)p_{n}^{k}\left(1-p_{n}\right)^{n-k}\;,\; k=0,1,\cdots,n,\; p_{n}\in\left(0,1\right). $ Show that, if the $ p_{n} $ have the property that $ np_{n}\rightarrow\lambda $ as $ n\rightarrow\infty $ , where $ \lambda $ is a positive constant, then the sequence $ \left\{ \mathbf{X}_{n}\right\} _{n\leq1} $ converges in distribution to a Poisson random variable $ \mathbf{X} $ with mean $ \lambda $ .

Hint:

You may find the following fact useful:

$ \lim_{n\rightarrow\infty}\left(1+\frac{x}{n}\right)^{n}=e^{x}. $

Solution

If $ \mathbf{X}_{n} $ converges to $ \mathbf{X} $ in distribution, then $ F_{\mathbf{X}_{n}}(x)\rightarrow F_{\mathbf{X}}(x) $$ \forall x\in\mathbf{R} $ , where $ F_{\mathbf{X}}(x) $ is continuous. This occurs iff $ \Phi_{\mathbf{X}_{n}}(\omega)\rightarrow\Phi_{\mathbf{X}}(\omega) $$ \forall x\in\mathbf{R} $ . We will show that $ \Phi_{\mathbf{X}_{n}}(\omega) $ converges to $ e^{-\lambda\left(1-e^{i\omega}\right)} $ as $ n\rightarrow\infty $ , which is the characteristic function of a Poisson random variable with mean $ \lambda $ .

$ \Phi_{\mathbf{X}_{n}}(\omega)=E\left[e^{i\omega\mathbf{X}_{n}}\right]=\sum_{k=0}^{n}e^{i\omega k}\left(\begin{array}{c} n\\ k \end{array}\right)p_{n}^{k}\left(1-p_{n}\right)^{n-k}=\sum_{k=0}^{n}\left(\begin{array}{c} n\\ k \end{array}\right)\left(p_{n}e^{i\omega}\right)^{k}\left(1-p_{n}\right)^{n-k} $$ =\left(p_{n}e^{i\omega}+1-p_{n}\right)^{n}=\left(1+p_{n}\left(e^{i\omega}-1\right)\right)^{n}. $

Now as $ n\rightarrow\infty $ , $ np_{n}\rightarrow\lambda\Rightarrow p_{n}\rightarrow\frac{\lambda}{n} $ .

$ \lim_{n\rightarrow\infty}\Phi_{\mathbf{X}_{n}}(\omega)=\lim_{n\rightarrow\infty}\left(1+p_{n}\left(e^{i\omega}-1\right)\right)^{n}=\lim_{n\rightarrow\infty}\left(1+\frac{\lambda}{n}\left(e^{i\omega}-1\right)\right)^{n}=e^{\lambda\left(e^{i\omega}-1\right)}=e^{-\lambda\left(1-e^{i\omega}\right)}, $

which is the characteristic function of Poisson random variable with mean $ \lambda $ .

c.f.

The problem 2 of the August 2007 QE [CS1QE2007August] is identical to this example.


Back to ECE600

Back to ECE 600 Exams

Alumni Liaison

Ph.D. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

Buyue Zhang