Probability and Measure;
Recall the definition:


Probability and Measure; Recall the definition: Show the following Xn → X and Yn y then...
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...
number 3 please
Hw4.1708.pd 1 2 TL (2) LP convergence vs. convergence in probability Let Xn, nNbe a sequence of random variables and let X be another random variable. Given l < p < oo, we say that Xn converges to X in Lp if E(Xn-X") → 0 as n → x Show that this implies that Xn converges to X in probability (3) Monte Carlo Let f : 10, 1] → R be continuous and let Xn, n on...
#s 2, 3, 6
2. Let (En)acy be a sequence in R (a) Show that xn → oo if and only if-An →-oo. (b) If xn > 0 for all n in N, show that linnAn = 0 if and only if lim-= oo. 3. Let ()nEN be a sequence in R. (a) If x <0 for all n in N, show that - -oo if and only if xl 0o. (b) Show, by example, that if kal → oo,...
2. Let Xn ~ NG, Intuitivel y, Xn will concentrate at as n -o. In this question, we will justify this intuition using the convergence concepts we learned (a) Show that Xn, 4> 1/2. (Recall for a random variable X which takes value 1/2 with all probability, its c.d.f. Fo is given by Fo(t) 0 for all t< 1/2, Fo(t)1 for all t 2 1/2. You need then to show the c.d.f. of Xn, say F(t), converges to Fo(t) at...
Consider two sequences of random variables X1, X2,... and Y1, Y., .... Suppose that Xn converges to a and Y, converges to b with probability 1. Show that X, + Y, converges to a+b, with probability 1. Next, we assume that the random variables Y cannot be equal to zero, show that X/Y, converges to a/b with probability 1.
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.
1) Let X and Y be random variables. Show that Cov( X + Y, X-Y) Var(X)--Var(Y) without appealing to the general formulas for the covariance of the linear combinations of sets of random variables; use the basic identity Cov(Z1,22)-E[Z1Z2]- E[Z1 E[Z2, valid for any two random variables, and the properties of the expected value 2) Let X be the normal random variable with zero mean and standard deviation Let ?(t) be the distribution function of the standard normal random variable....
5. Let {xn} and {yn} be sequences of real numbers such that x1 =
2 and y1 = 8 and for n = 1,2,3,···
x2nyn + xnyn2 x2n + yn2 xn+1 = x2 + y2 and yn+1 = x + y .
nn nn
(a) Prove that xn+1 − yn+1 = −(x3n − yn3 )(xn − yn) for all
positive integers n.
(xn +yn)(x2n +yn2) (b) Show that 0 < xn ≤ yn for all positive
integers n.
Hence, prove...
4. Let X1, X2, . .. be independent random variables satisfying E(X) E(Xn) --fi. (a) Show that Y, = Xn - E(Xn) are independent and E(Yn) = 0, E(Y2) (b) Show that for Y, = (Y1 + . . + Y,)/n, <B for some finite B > 0 and VB,E(Y) < 16B. 16B 6B 1 E(Y) E(Y) n4 i1 n4 n3 (c) Show that P(Y, > e) < 0 and conclude Y, ->0 almost surely (d) Show that (i1 +...
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...