Let Xn be a sequence of Rvs with common finite variance σ∧2. Suppose that the correlation coefficient between Xi and Xj is<0 for all i ≠j. Show that the WLLN holds for the sequence {Xn}.
Let Xn be a sequence of Rvs with common finite variance σ∧2. Suppose that the correlation...
5.2.5 (Example 5.2.6 Continued) Suppose thatXY are iid having the following common distribution. PC,-1-cip.i-1. 2. 3. and 2 <p < 3 Here. c c(p) (> 0) is such that Σ | P(X,-i) = 1.. Is there a real number a = a(p) such that Xn → a as n → 00, for all fixed 2 <p < 3? FYI: Example 5.2.6 In order to appreciate the importance of Khinchine's WLLN (Theorem 5.2.3). let us consider a sequence of iid random...
Exercise 5.23. Let (Xn)nz1 be a sequence of i.i.d. Bernoulli(p) RVs. Let Sn -Xi+Xn (i) Let Zn-(Sn-np)/ V np (1-p). Show that as n oo, Zn converges to the standard normal RV Z~ N(0,1) in distribution. (ii) Conclude that if Yn~Binomial(n, p), then (iii) From i, deduce that have the following approximation x-np which becomes more accurate as n → oo.
Let {Xn} be a sequence of RVs with Xn~G(n,β), where β>0 is a constant (independent of n). Find the limiting distribution of Xn/n.
2. Suppose that ξι, ξ2, . . . are 1.1.d. RVs with Εξι-μ and Var (6)-σ2 E (0,00). Set X-3kE+2,1,2,, and let Sn X+Xn, n21 (a) Compute EXk, Var (Xk) and Cov (Xj Xk) for j k (b) Find the limit lim P r E R nVar (X1) 72 →00 as a sum of independent RVs. From the form of the expression in (1), one could expect that the answer will be in terms of the standard normal DF 1,...
3. Let X1, X2, . . . , Xn be random variables with a common mean μ. Sup- pose that cov[Xi, xj] = 0 for all i and A such that j > i+1. If 仁1 and 6 VECTORS OF RANDOM VARIABLES prove that = var X n(n- 3)
4) Let Xi , X2, . . . , xn i id N(μ, σ 2) RVs. Consider the problem of testing Ho : μ- 0 against H1: μ > 0. (a) It suffices to restrict attention to sufficient statistic (U, v), where U X and V S2. Show that the problem of testing Ho is invariant under g {{a, 1), a e R} and a maximal invariant is T = U/-/ V. (b) Show.that the distribution of T has MLR,...
Xn are independent normal variates with the same variance σ, but with Suppose that Xi, X2, different means, Xi ~N(pbi,ơ2), for i-1.2, n where bi, b,.. k constants. (a) Find expressions for the MLE of μ and σ. You need not show the second derivative conditions (b) Suppose that b,-b2-...-bn. Find a simplified expression for the MLE of μ (c) Suppose that b,-b2-...-bn-1, and , is known. Find the MLE ofơ
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.
5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J, 1,,-1, , n. OV&.for any two random variables X and Y) or each 1, and (11 CoV(X,Y) var(x)var(y) (Recall that p vararo 5. Let X1,X2, . , Xn be a random sample from a distribution with finite variance. Show that (i) COV(Xi-X, X )-0 f ) ρ (Xi-XX,-X)--n-1, 1 # J,...
Let X1,X2, , Xn be a random sample from a normal distribution with a known mean μ (xi-A)2 and variance σ unknown. Let ơ-- Show that a (1-α) 100% confidence interval for σ2 is (nơ2/X2/2,n, nơ2A-a/2,n). Let X1,X2, , Xn be a random sample from a normal distribution with a known mean μ (xi-A)2 and variance σ unknown. Let ơ-- Show that a (1-α) 100% confidence interval for σ2 is (nơ2/X2/2,n, nơ2A-a/2,n).