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74. Let X1, X2, ... be a sequence of independent identically distributed contin- uous random variables....
Let X1, , X2 ... be a sequence of independent and identically distributed continuous random variables. Say that a peak occurs at time n if Xn-1 < Xn < Xn+1 . Argue that the proportion of time that a peak occurs is, with probability 1, equal to 1/3
2. Let X1, X2,. . , Xn denote independent and identically distributed random variables with variance σ2, which of the following is sufficient to conclude that the estimator T f(Xi, , Xn) of a parameter 6 is consistent (fully justify your answer): (a) Var(T) (b) E(T) (n-1) and Var(T) (c) E(T) 6. (d) E(T) θ and Var(T)-g2. 72 121
(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...
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,...,Xn denote independent and identically distributed random variables with variance 2. Which of the following is sucient to conclude that the estimator T = f(X1,...,Xn) of a parameter ✓ is consistent (fully justify your answer): (a) Var(T)= (b) E(T)= and Var(T)= . (c) E(T)=. (d) E(T)= and Var(T)= We were unable to transcribe this imageWe were unable to transcribe this imageoe We were unable to transcribe this imageWe were unable to transcribe this imageWe were unable to transcribe this...
18. Let X, X2, ..., Xv are independent and identically distributed standard uniform random variables. Find the following expectations: (a) E[max(X,,X2, .Xn,)] (b) E[min(X1,X2,..., Xn)]
15. Let X,, X2,.. . be independent, identically distributed random variables, EIXI oo, and denote S,-X1+... + Xn. Prove that [Use symmetry in the final step.] 15. Let X,, X2,.. . be independent, identically distributed random variables, EIXI oo, and denote S,-X1+... + Xn. Prove that [Use symmetry in the final step.]
13. Let X1, X2, ...,Xy be a sequence of independent and identically distributed discrete random variables, each with probability mass function P(X = k)=,, for k = 0,1,2,3,.... emak (a) Find the expected value and the variance of the sample mean as = N&i=1X,. (b) Find the probability mass function of X. (c) Find an approximate pdf of X when N is very large (N −0).
8. Let X1...., X, be i.i.d. ~E(1) random variables (i.e., they are independent and identically distributed, all with the exponential distribution of parameter 1 = 1). a) Compute the cdf of Yn = min(X1,...,xn). b) How do P({Y, St}) and P({X1 <t}) compare when n is large and t is such that t<? c) Compute the odf of Zn = max(X1...., X.). d) How do P({Zn2 t}) and P({X1 2 t}) compare when n is large and t is such...
1. Let X1, ..., Xn, Y1, ..., Yn be mutually independent random variables, and Z = + Li-i XiYi. Suppose for each i E {1,...,n}, X; ~ Bernoulli(p), Y; ~ Binomial(n,p). What is Var[Z]?