C. Let X, X2, be uncorrelated random variables, where X has a distribution given by P(X,...
4. Let Xi, X2,... be uncorrelated random variables, such that Xn has a uniform distribution over -1/n, 1/n]. Does the sequence converge in probability? 5. Let Xi,X2 be independent random variables, such that P(X) PX--) Does the sequence X1 +X2+...+X satisfy the WLLN? Converge in probability to 0?
Let X1, X2,..., X, be n independent random variables sharing the same probability distribution with mean y and variance o? (> 1). Then, as n tends to infinity the distribution of the following random variable X1 + X2 + ... + x, nu vno converges to Select one: A. an exponential distribution B. a normal distribution with parameters hi and o? C a normal distribution with parameters 0 and 1 D. a Poisson distribution
E. Let Xi, X2, be independent random variables from a geometric distribution with parameter 0.1. Verify, whether the sequence n1,2, n+ 31 converges almost surely and if yes, find the limit.
D. Let Xi, X2,. be independent random variables from a uniform distribution over the interval [0, 1]. Prove that the sequence X+XX. converges in probability and find the limit
8. Let X be a continuous random variable with mgf given by It< 1 M(t)E(eX) 1 - t2 (a) Determine the expected value of X and the variance of X [3] (b) Let X1, X2, ... be a sequence of iid random variables with the same distribution as X. Let Y X and consider what happens to Y, as n tends to oo. (i) Is it true that Y, converges in probability to 0? (Explain.) [2] (ii) Explain why Vn...
1. Let X1, X2,... be independent random variables each with the standard normal distribution, and for each n 0 let Sn 너 1 i. Use importance sampling to obtain good estimates for each of the following probabilities: (a) P[maxns 100 Sn > 10); and (b) P[maxns100 Sn > 30 HINTS: The basic identity of importance sampling implies that n100 where Po is the probability measure under which the random variables Xi, X2,... are independent normals with mean 0 amd variance...
(10 marks) Let X1, X2,... be a sequence of independent and identically distributed random variables with mean EX1 = i and VarX1 = a2. Let Yı, Y2, ... be another sequence of independent and identically distributed random variables with mean EY = u and VarY1 a2 Define the random variable ( ΣxΣ) 1 Dn 2ng2 i= i=1 Prove that Dn converges in distribution to a standard normal distribution, i.e., prove that 1 P(Dn ) dt 2T as n >oo for...
4. Let 8 >0. Let X, X2,..., X, be a random sample from the distribution with probability density function S(*;ð) - ma t?e-vor x>0, zero otherwise. Recall: W=vX has Gamma( a -6, 0-ta) distribution. Y=ZVX; = Z W; has a Gamma ( a =6n, = ta) distribution. i=1 E(Xk) - I( 2k+6) 120 ok k>-3. 42 S. A method of moments estimator of 8 is 42.n 8 = h) Suggest a confidence interval for 8 with (1 - 0) 100%...
8. Let X1, X2,...,X, U(0,1) random variables and let M = max(X1, X2,...,xn). - Show that M. 1, that is, M, converges in probability to 1 as n o . - Show that n(1 - M.) Exp(1), that is, n(1 - M.) converges in distribution to an exponential r.v. with mean 1 as n .
1. Let Xi, X2,... be independent random variables each with the standard normal distribution, and for each n 2 0 let Sn-1 Xi. Use importance sampling to obtain good estimates for each of the following probabilities: (a) Pfmaxn<100 Sn> 10; and (b) Pímaxns100 Sn > 30) HINTS: The basic identity of importance sampling implies that d.P n100 where Po is the probability measure under which the random variables Xi, X2,... are independent normals with mean 0 amd variance 1. The...