TOPIC:Convergence in probability.
Y. Show that if P(Y 0) = 0, then Let Xn--X and Y, Y. r2 Y. Show that if P(Y 0) = 0, then Let Xn--X and Y, Y. r2
Let X = {(x, y) ∈ R2: x ≥ 0 or y = 0}; and let τ be the subspace topology on X induced by the usual topology on R2 . Suppose R has the usual topology and we define f : X → R by f((x, y)) = x for each (x, y) ∈ X. Show that f is a quotient map, but it is neither open nor closed.(So, a restricted function of an open function need not be...
2. Let {xn}nEN be a sequence in R converging to x 0. Show that the sequence R. Assume that x 0 and for each n є N, xn converges to 1. 3. Let A C R". Say that x E Rn is a limit point of A if every open ball around x contains a point y x such that y E A. Let K c Rn be a set such that every infinite subset of K has a limit...
#s 2, 3, 6 2. Let (En)acy be a sequence in R (a) Show that xn → oo if and only if-An →-oo. (b) If xn > 0 for all n in N, show that linnAn = 0 if and only if lim-= oo. 3. Let ()nEN be a sequence in R. (a) If x <0 for all n in N, show that - -oo if and only if xl 0o. (b) Show, by example, that if kal → oo,...
4. Let X1, X2, . .. be independent random variables satisfying E(X) E(Xn) --fi. (a) Show that Y, = Xn - E(Xn) are independent and E(Yn) = 0, E(Y2) (b) Show that for Y, = (Y1 + . . + Y,)/n, <B for some finite B > 0 and VB,E(Y) < 16B. 16B 6B 1 E(Y) E(Y) n4 i1 n4 n3 (c) Show that P(Y, > e) < 0 and conclude Y, ->0 almost surely (d) Show that (i1 +...
Let Xi, X2...-Xn be a iid. sample from Bernoulli(p) and let Yn-Σηι(X-P)/n. Show that Ya converges to a degenerate distribution at 0 as n-o.
n. 7. Let Xi, , Xn be iid ;0) =-e-r2/0 where x > 0. Sho w that θ=「x? is based on f (x efficient.
Let V = P1(R) and W = R2. Let B = (1,x) and y=((1,0), (0, 1)) be the standard ordered bases for V and W respectively. Define a linear map T:V + W by T(P(x)) = (p(0) – 2p(1), p(0) + p'(0)). (a) Let FEW* be defined by f(a,b) = a – 26. Compute T*(f). (b) Compute [T]y,ß and (T*]*,y* using the definition of the matrix of a linear transformation.
1) Let X and Y be random variables. Show that Cov( X + Y, X-Y) Var(X)--Var(Y) without appealing to the general formulas for the covariance of the linear combinations of sets of random variables; use the basic identity Cov(Z1,22)-E[Z1Z2]- E[Z1 E[Z2, valid for any two random variables, and the properties of the expected value 2) Let X be the normal random variable with zero mean and standard deviation Let ?(t) be the distribution function of the standard normal random variable....
Let X be distributed as N(0, 1). Define Xn (1)"X, n 1,2, a. [3 pts] Show that Xn-X. b. [3 pts] Show that Xn -» X
7. Let X1 , Xn be i.i.d. with the density p(r,0) = a*(1 - 0)1-k1{x = 0,1} (a) Find the ML estimator of 0 (b) Is it unbiased? (c) Compute its MSE 7. Let X1 , Xn be i.i.d. with the density p(r,0) = a*(1 - 0)1-k1{x = 0,1} (a) Find the ML estimator of 0 (b) Is it unbiased? (c) Compute its MSE