Difference between revisions of "ECE-QE CS1-2012" - Rhea

Revision as of 15:43, 25 January 2014

Question

Part 1. 25 pts

State and prove the Chebyshev inequality for random variable $ \mathbf{X} $ with mean $ \mu $ and variance $ \sigma^2 $. In constructing your proof, keep in mind that $ \mathbf{X} $ may be either a discrete or continuous random variable.

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Part 2. 25 pts

Let $ \mathbf{X}_{1} \dots \mathbf{X}_{n} \dots $ be a sequence of independent, identical distributed random variables, each uniformly distributed on the interval [0, 1], an hence having pdf
$ f_{X}\left(x\right)=\begin{cases} \begin{array}{lll} 1, \text{ for } 0 \leq x \leq1\\ 0, \text{ elsewhere. } \end{array}\end{cases} $

Let $ \mathbf{Y}_{n} $ be a new random variable defined by

$ \mathbf{Y}_{n} = min \,\{{ \mathbf{X}_1, \mathbf{X}_2, \dots \mathbf{X}_n} \} $

(a) Find the pdf of $ \mathbf{Y}_{n} $

(b) Does the sequence $ \mathbf{Y}_{n} $ converge in probability?

(c) Does the sequence $ \mathbf{Y}_{n} $ converge in distribution? If yes, specify the cumulative function of the random variable it converges to.

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