Suppose that X1,X2,... is a sequence of i.i.d. r.v.s having uniform distribution on [0,1]. Define Yn=n(1−max1≤i≤nXi) for n=1,2,.... Prove that Yn converges in distribution to an exponential distribution.
Suppose that X1,X2,... is a sequence of i.i.d. r.v.s having uniform distribution on [0,1]. Define Yn=n(1−max1≤i≤nXi)...
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 3: Suppose X1, X2, is a sequence of i.i.d. random variables having the Poisson distribution with mean λ. Let A,-X, (a) Is λη an unbiased estimator of λ? Explain your answer. (b) Is in a consistent estimator of A? Explain your answer 72
suppose X1 -> Xn is a random sample from a uniform distribution on the interval [0,theta]. let X1 = min {X1,X2,...Xn} and let Yn= nX1. show that Yn converges in distribution to an exponential random variable with mean theta.
3. Consider n i.i.d. r.v.s. X1, .Xn, where X, Bin(p). Show that the conditional PMF of (Xi, X2, -.. , X) given a number of successes, where a; E 10,1 に! is uniform
7. Let Z be Gaussian white noise, i.e. Z is a sequence of i.i.d. normal r.v.s each with mean zero and variance 1. Define Zt, t(-1- 1)/v2, if t is odd Show that Xis WN(0,1) (that is, variables Xt and Xt+k,k2 1, are uncorrelated with mean zero and variance 1) but that Xt and Xi-i are not i.i.d
7. Let Z be Gaussian white noise, i.e. Z is a sequence of i.i.d. normal r.v.s each with mean zero and variance...
Let X1, X2, · · · Xn be a i.i.d. sample from Bernoulli(p) and let . Show that Yn converges to a degenerate distribution at 0 as n → ∞.
Exercise 5.22. Let (Xn)nel be a sequence of i.i.d. Poisson(a) RVs. Let Sn-X1++Xn (i) Let Zn-(Sn-nA)/Vm. Show that as n-, oo, Zn converges to the standard normal RV Z ~ N(0,1) in distribution (ii) Conclude that if Yn~Poisson(nX), then ii) Fromii) deduce that we have the following approximation which becomes more accurate as noo.
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 .
(a) Suppose that i, X2,... , In is an i.i.d. sample from Exp(1). Show that, for a standard normal random variable Z b) Show Г(n) by differencing both sides of the approximation in part a. Then set a -0 to get Stirling's Formula. 5. Suppose that Y is an id sample from Negative Binomial (n,p). Give a normal approximation of Yn use CLT, when n is large. 6. (Mandatory for Graduate Student. Extra credit for undergrad.) Let Ai, converges to...
Problem 5.1 (Relation between Gaussian and exponential) Suppose that Xi and X, are i.i.d. N(0,1) (a) Show that Z-X1 + X is exponential with mean 2. b) True or False: Z is independent of Θ-tan ( -i Hint: Use the results from Example 5.4.3, which tells us the joint distribution of V and Θ.