9. In many cases where a sequence of random variables converges in probability to some b,...
Consider a sequence of random variables X1, . . . , Xn, . . .where for each n, Xn ∼ t distribution. Apply Slutsky’s Theorem to show that as the degrees of freedom go to infinity, the distribution converges to a standard normal. (a) Let V1, . . . , V_n, . . . be such that Vn ∼ Chi Sq, n df. Find the value b such that V/n in probability −→ b. (b) Letting U ∼ N(0, 1),...
Consider a sequence of random variables X1, ..., Xn, ..., where for each n, Xn~ tn. We will use Slutsky's Theorem to show that as the degrees of freedom go to infinity, the distribution converges to a standard normal. (a) Let V1, ..., Vn, ... be such that Vn ~ X2. Find the value b such that Vn/n þy b. (b) Letting U~ N(0,1), show that In = ☺ ~tn and that Tn "> N(0,1). VVn/n
Write out a sequence of random variables {Xn}, n=1,2,…such that Xn converges to 0 in probability but {E(Xn), n=1,2,…} does not converge to 0. Prove it.
Let Z1, Z2, . . . be a sequence of independent standard normal random variables. Define X0 = 0 and Xn+1 = (nXn + (Zn+1))/ (n + 1) , n = 0, 1, 2, . . . . The stochastic process {Xn, n = 0, 1, 2, } is a Markov chain, but with a continuous state space. (a) Find E(Xn) and Var(Xn). (b) Give probability distribution of Xn. (c) Find limn→∞ P(Xn > epsilon) for any epsilon > 0.
4. Let Z1, Z2,... be a sequence of independent standard normal random variables. De- fine Xo 0 and n=0, 1 , 2, . . . . TL: n+1 , The stochastic process Xn,n 0, 1,2,3 is a Markov chain, but with a continuous state space. (a) Find EXn and Var(X). (b) Give probability distribution of Xn (c) Find limn oo P(X, > є) for any e> 0. (d) Simulate two realisations of the Markov process from n = 0 until...
Prove that a sequence of random variables X1, X2, ... converges in probability to a constant μ if and only if it also converges in distribution to μ. 5. Prove that a sequence of random variables X1, X2,... converges in probability to a constant p if and only if it also converges in distribution to u.
1. A sequence of random variables Xn satisfy Xn _>X in probability and E(Xn) -> E(X) for some random variable X (a) Show that E([X, - X|) -> 0 if Xn >0 for all n (b) Find a counterexample satisfying E(X,n - X) A0 if X are not non-negative. 1. A sequence of random variables Xn satisfy Xn _>X in probability and E(Xn) -> E(X) for some random variable X (a) Show that E([X, - X|) -> 0 if Xn...
Let X1, X2,...be a sequence of random variables. Suppose that Xn?a in probability for some a ? R. Show that (Xn) is Cauchy convergent in probability, that is, show that for all > 0 we have P(|Xn?Xm|> )?0 as n,m??.Is the converse true? (Prove if “yes”, find a counterexample if “no”)
3. (a) (5 points) Let Xi,... be a sequence of independent identically distributed random variables e of tnduqendent idente onm the interval (o, 1] and let Compute the (almost surely) limit of Yn (b) (5 points) Let X1, X2,... be independent randon variables such that Xn is a discrete random variable uniform on the set {1, 2, . . . , n + 1]. Let Yn = min(X1,X2, . . . , Xn} be the smallest value among Xj,Xn. Show...
C. Let X, X2, be uncorrelated random variables, where X has a distribution given by P(X, =-n) = P(X, = n) = 1/(2㎡), P(X, = 0) = 1-1/n2 for n > i. Verify whether the sequence Xitx2 + : : : + X", n=1, 2, , converges in probability.