a sequence of random variables X1, X2, .... Suppose X, follows a discrete distri- bution taking two possible values {0, n²}, with P(X, = 0) = 1 – and P(X, = n²) =. Calculate E(Xn). What happens to E(X„) as n → 0? Does X, converge in probability to some real number? If so, find that number and prove. If not, explain.
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- 5. Let the random variable Y,, have a distribution that is b(n, p). Prove that (Y/n)(1-Y/n) converges in probability to p(1-p).Let P be a random variable having a uniform distribution with minimum 0 and maximum 3 i.e. P ~ Uniform(0, 3). Let Q = log (P/(3-P)). Find E[P]. You areexpected to solve this problem without using Method of Transformation.Let X1, X2, ..., X, be independent and identically distributed random variables such that P(X1 = 1) = P(X1 =-1) = }. E}-1a;Xj, where Derive the moment generating function of the random variable Yn a1 , a2, . , ɑn are constants. In the special case aj = 2-i for j > 1, show that Yn converges in distribution as n → o to the uniform distribution on the interval (–1, 1).
- Cars pass a certain street location according to a Poisson process with rate 2. A woman who wants to cross the street at that location waits until she can see that no cars will come by in the next T time units. а) b) Find the probability that her waiting time is 0. Find the expected waiting time is 0. e. -(1+ a7)] A) В) -(1+7)] et, [e" - (1– AT)] C) [eT -(1– 27)] D)(b) Let {X;}"-1 be i.i.d. uniform random variables in [0,0], for some 0 > 0. Denote by Mn = max;=1,2,.n X;. • Prove that M, converges in probability to 0.Let X1, X2, X3, . . . be a sequence of independent Poisson distributed random variables with parameter 1. For n ≥ 1 let Sn = X1 + · · · + Xn. (a) Show that GXi(s) = es−1.(b) Deduce from part (a) that GSn(s) = ens−n.
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