7. Let X1, X2, ... be an i.i.d. random variables. (a) Show that max(X1,... , X,n)/n...
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...
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
v Problem 5 Let Xi, і ї, , n, n-256, be i.i.d. Pois(1)-random variables, and Sn- il Xi. a) Using Chebychev's inequality, estimate the probability that P(Sn > 2E S]).
6. Let X1, X2,.. , Xn denote a random sample of size n> 1 from a distribution with pdf f(x; 6) = 6e-8, 0<x< 20, zero elsewhere, and 0 > 0. Le Y = x. (a) Show that Y is a sufficient and complete statistics for . (b) Prove that (n-1)/Y is an unbiased estimator of 0.
Let X1, X2, ... be independent continuous random variables with a common distribution function F and density f. For k > 1, let Nk = min{n>k: Xn = kth largest of X1, ... , Xn} (a) Show Pr(Nx = n) = min-1),n>k. (b) Argue that fxx, (a) = f(x)+(a)k-( ++2)(F(x)* (c) Prove the following identity: al= (+*+ 2) (1 – a)', a € (0,1), # 22. i
Exercise 1 Consider utility maximization problem: Kuhn Tucker Theorem max U (x1, x2) = x1+x2 21,22 subject to Tị 2 0, r2 > 0, p1x1+p2r2 < I, where p1, p2 and I are positive constants. Exercise 1 Consider utility maximization problem: Kuhn Tucker Theorem max U (x1, x2) = x1+x2 21,22 subject to Tị 2 0, r2 > 0, p1x1+p2r2 < I, where p1, p2 and I are positive constants.
Let X1, X2, ..., Xn be iid random variables with a "Rayleigh” density having the following pdf: 22 -12 10 f(x) = e x > 0 > 0 0 пе a) (3 points) Find a sufficient estimator for 0 using the Factorization Theorem. b) (3 points) Find a method of moments estimator for 0. Small help: E(X1) = V c) (7 points) What is the MLE of 02 + 0 – 10 ? d) (7 points) For a fact, 21–1...
3. Let X1, X2, . . . , Xn be a random sample from a distribution with the probability density function f(x; θ) (1/02)Te-x/θ. O < _T < OO, 0 < θ < 00 . Find the MLE θ
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 .
Let X1, X2,.. .Xn be a random sample of size n from a distribution with probability density function obtain the maximum likelihood estimator of θ, θ. Use this maximum likelihood estimator to obtain an estimate of P[X > 4 when 0.50, 2 1.50, x 4.00, 4 3.00.