Question

Suppose that

Suppose that \(\left(\xi_{j}\right)^{\infty}=1\) is a sequence of independent identically distributed \((i . i . d .)\) continuous random variables.

- Suppose that each \(\xi_{i}\) has a probability density function \(p_{i}(x)=\left\{\begin{array}{c}\frac{\beta}{x^{x}}, x \geq 1 \\ 0, x<1\end{array}\right.\), where \(\alpha, \beta \in\)

R.

- Let \(S_{n}=\sum_{i=1}^{n} \xi_{i}\).

- Let \(S_{n}=\frac{s_{n}-\operatorname{mE}\left(\xi_{0}\right)}{\sqrt{n} \operatorname{Var}(\mathcal{B})}\).

a. Find a condition on \(\alpha\) and a condition on \(\beta\) (as a function of \(\alpha\) ) which together make \(f_{1}(x)\) a probability density function.

b. Find conditions on \(\alpha\) which guarantee that \(\lim _{n \rightarrow \infty} S_{n}\) will converge in distribution to a standard normal random variable. (Hint: what are the hypotheses of the central limit theorem?)

c. Find the probability density function for \(Z=\xi_{1}+\xi_{2}\), where each \(\xi\), has density function \(p_{i}(x)=\left\{\begin{array}{r}e^{-x}, x \geq 0 \\ 0, x<0\end{array}\right.\), To eam full credit, you must simplify your answer as much as possible.

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