Problem 3. (Law of Large Number and Moving Average Model) Let s0, E1, E2, be a...
Law of Large Number↓ Led tin eperaje Theorem 9.11. (Central limit theorem) Suppose that we have i.i.d. random variables Xi,X2. X3,... with finite mean EX and finite variance Var(X) = σ2. Let Sn-Xi + . . . + Xn. Then for any fixed - oo<a<b<oo we have lim Pax (9.6) Theorem 4.8. (Law of large numbers for binomial random variables) For any fixed ε > 0 we have (4.7) n-00
Let X1, X2, X3, . be a sequence of i.i.d. Uniform(0,1) random variables. Define the sequence Yn as Ymin(X1, X2,,Xn) Prove the following convergence results independently (i.e, do not conclude the weaker convergence modes from the stronger ones). d Yn 0. a. P b.Y 0. L 0, for all r 1 Yn C. a.s d. Y 0. Let X1, X2, X3, . be a sequence of i.i.d. Uniform(0,1) random variables. Define the sequence Yn as Ymin(X1, X2,,Xn) Prove the following...
Problem 2. (Conditional Distribution of MVN) Let Z1, Z2, Z3 be i.i.d. N(0,1) dis- tributed random variables, and set X1 = 21 – Z3 X2 = 2Z1 + Z2 – 223 X3 = -221 +3Z3 1) What distribution does X = (X1, X2, X3)T follow? Specific the parameters. 2) Find out P(X2 > 0|X1 + X3 = 0).
Problem 1. (Bivariate Normal Distribution) Let Z1, Z2 be i.i.d. N(0,1) distributed random variables, and p be a constant between –1 and 1. define X1, X2 as: x3 = + VF5223X = v T14:21 - VF52 23 1) Show that, (X1, X2)T follows bivariate Normal distribution, find out the mean vector and the covariance matrix. 2) Write down the moment generating function, and show that when p= 0, X11X2.
Central Limit Theorem: let x1,x2,...,xn be I.I.D. random variables with E(xi)= U Var(xi)= (sigma)^2 defind Z= x1+x2+...+xn the distribution of Z converges to a gaussian distribution P(Z<=z)=1-Q((z-Uz)/(sigma)^2) Use MATLAB to prove the central limit theorem. To achieve this, you will need to generate N random variables (I.I.D. with the distribution of your choice) and show that the distribution of the sum approaches a Guassian distribution. Plot the distribution and matlab code. Hint: you may find the hist() function helpful
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...
Let X1, X2, X3 … be independent random variable with P(Xi = 1) = p = 1-P(Xi=0), i ≥ 1. Define: N1 = min {n: X1+…+ Xn =5}, N2 = 3 if X1 = 0, 5 if X1 = 1. N3 = 3 if X4 = 0, 2 if X4 = 1. Which of the Ni are stopping times for the sequence X1, …?
Problem 3 Let Xi, X2,... , Xn be a sequence of binary, i.i.d. random variables. Assume P (Xi 1) P (Xi = 0) = 1/2. Let Z be a parity check on seluence Xi, X2, ,X,, that is, Z = X BX2 e (a) Is Z statistically independent of Xi? (Assume n> 1) (b) Are X, X2, ..., Xn 1, Z statistically independent? (c) Are X, X2,.., Xn, Z statistically independent? (d) Is Z statistically independent of Xi if P...
L.1) BinomialDist[1, p] random variables In what context do random variables with BinomialDist[1, p] arise? L.2) Expected value and Variance for the Binomial[1, p] and Binomial[n, p] random variables a) Go with a random variable X with BinomialDist[1, p Calculate Expect[X] and Var[X]. b) Go with a random variable X with BinomialDist[n, p]. Use the fact that X is the sum of n independent random variables each with BinomialDist[1, pl to explain why: Expect[x]-n p and Var[X]-np(p) L.3) Relations among...
Can someone please help me with this problem? Thank you in advance! 3. (10 points) Let X1, X2, ... be a sequence of random variables with common uniform distribution on (0,1). Also, let Zn = (11=1 X;)/n be the geometric mean of X1, X2, ..., Xn, n=1,2,.... Show that In , where c is some constant. Find c.