Let X1,.-. , Xn ~ N(2, 1) be independent, where E R is unknown. (i) Show that X := -1X; is a minimum sufficient statistic. (ii) Show that X is a complete statistic.
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 +...
10. Let T1 and T2 be two topologies on a set X. Then T1 is said to be a finer topology than T2 (and T2 is said to be a coarser topology than T1) if Ti2 T2. Prove that (i) the Euclidean topology R is finer than the finite-closed topology on R; (ii) the identity function f: (X, Ti) -(X, T2) is continuous if and only if TI is a finer topology than T2.
Consider a random sample (X1, Y1),(X2, Y2), . . . ,(Xn, Yn) where Y | X = x is modeled by a N(β0 + βx, σ2 ) distribution, where β0, β1 and σ 2 are unknown. (a) Prove that the mle of β1 is an unbiased estimator of β1. (b) Prove that the mle of β0 is an unbiased estimator of β0.
6. Show that the followings define metrics on R2: For r = (11, 12), y = (y1, y2) ER, the company = 139-un +100 - 247 91.42.), y =(1,9) ER di(x,y) = |21 - y1| + |22 - y2), doo (I, y) = max{\21 – yı], \12 - y2|}.
Problem 5. For u = (Uk)x=1,2,... El, we set Tnu = (U1, U2, ..., Un, 0,...). (1) Prove that Tn E B(C2, (). (2) We define the operator I as Iu = u (u € 14). Then, prove that for any u ele, lim ||T,u - Tulee = 0. (3) Prove that I, does not converge to I with respect to the norm of B(C²,1). Let X, Y be Banach spaces. Definition (review) We denote by B(X, Y) a set...
1. Let (N(t))>o be a Poisson process with rate X, and let Y1,Y2, ... bei.i.d. random variables. Fur- ther suppose that (N(t))=>0 and (Y)>1 are independent. Define the compound Poisson process N(t) Y. X(t) = Recall that the moment generating function of a random variable X is defined by ºx(u) = E[c"X]. Suppose that oy, (u) < for all u CR (for simplicity). (a) Show that for all u ER, ºx() (u) = exp (Atløy, (u) - 1)). (b) Instead...
#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,...
Let Y1,Y2, …… Yn be a random sample from the distribution f(y) = θxθ-1 where 0 < x < 1 and 0 < θ < ∞. Show that the maximum likelihood estimator (MLE) for θ is
I am struggling with part (ii) Let g(x, y) (e" +1)2+2(e-e(e1). 22-1 For any fixed x E R, show that the equation g(x,y) = 0 admits a solution y(x) > 0, and limx-0 y(x) = 0. (ii) Show that there exists a constant y > 0, such that for any fixed y E [0, ] the equation g(x,y) = 0 admits a solution 2(y).