Let be a sequence of random variables, and let Y be a random variable on the same sample space. Let An(ϵ) be the event that |Yn − Y | > ϵ. It can be shown that a sufficient condition for Yn to converge to Y w.p.1 as n → ∞ is that for every ϵ > 0,
(a) Let be independent uniformly distributed random variables on [0, 1], and let Yn = min(X1, . . . , Xn). In class, we showed that Yn → 0 w.p.1. Prove the same result by using the sufficient condition given above.
(b) Let be exponential random variables with parameter α that are not necessarily independent, and let Vn = Zn/n. Use the sufficient condition above to show that Vn → 0 w.p.1.
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Let be a sequence of random variables, and let Y be a random variable on the...
1. Let {y,)%, be a sequence of random variables, and let Y be a random variable on the same sample space. Let A(E) be the event that Y - Y e. It can be shown that a sufficient condition for Y, to converge to Y w.p.1 as n → oo is that for every e0, (a) Let {Xbe independent uniformly distributed random variables on [0, 1] , and let Yn = min (X), , X,). In class, we showed that...
Let X and Y be independent random variables with . Assume that and . Demonstrate that Cov(X,Y) = 0 We were unable to transcribe this imageWe were unable to transcribe this image400 OC
Let be independent, identically distributed random variables with . Let and for , . (a) Show that is a martingale. (b) Explain why satisfies the conditions of the martingale convergence theorem (c) Let . Explain why (Hint: there are at least two ways to show this. One is to consider and use the law of large numbers. Another is to note that with probability one does not converge) (d) Use the optional sampling theorem to determine the probability that ever attains...
Let be a sequence of independent random variables with and . Show that in probability, We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
Let be independent random variables, where ~, Is sufficient for ? We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imagePoi(ix) 2 We were unable to transcribe this imageWe were unable to transcribe this image
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Let and be two Gaussian random variables. (1) Sketch the PDFs of , on the same chart. (2) Assuming , are independent, compute . X1N(4.2,1) X2~ N(12,70 We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this image
Let Y.Y2, ,Yn be independent standard normal random variables. That is, Y i-1,... ,n, are iid N(0, 1) random variables. 25 a) Find the distribution of Σ 1 Y2 b) Let Wn Y?. Does Wn converge in probability to some constant? If so, what is the value of the constant?
Let , be independent N(0,1) distributed random variables. Define and . Without using calculus, show that . We were unable to transcribe this imageWe were unable to transcribe this imageW1 = x + x x1 - x x} + Xž We were unable to transcribe this image