For any sequence of RVs {Xn}. Show that max|Xk|→0 in pr →n^-1Sn→0 in pr.
For any sequence of RVs {Xn}. Show that max|Xk|→0 in pr →n^-1Sn→0 in pr.
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.
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}.
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.
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.
Exercise 2 (Monte Carlo integration). Let (Xk)kzl be i.i.d. Uniform([0, 1]) RVs and let f: [0,1] -- R be a continuous function. For each n2 1, let (f(X)f(X2).+f(Xn)) (3) In = -- .. + Sof(x) dx in probability. (i) Suppose o f (x)| dx (ii) Further assume that f lf(x)2 dx <o0. Use Chebyshef's inequality to show that :< oo. Show that In P (IIn-I2 alVnVar(f(X1)) a2 f(x)2 dx (4)
2. Let Xn, n > 1, be a sequence of independent r.v., and Øn (t) = E (eitX»), ER be their characteristic functions. Let Yn = {k=0 Xk, n > 0, X0 = 0, and 8. () = {1*: (),ER. k = 1 a) Let t be so that I1=1 løk (t)) > 0. Show that _exp{itYn} ?, n > 0, On (t) is a martingale with respect to Fn = (Xo, ...,Xn), n > 0, and sup, E (M,|2)...
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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,...
Exercise 2.1.18 (Easy): Let {xn} be a sequence and x ∈ R. Suppose that for any ε > 0, there is an M such that for all n ≥ M, |xn \ x| ≤ ε. Show that lim(xn)= x.
Show that for every n ∈ N and every sequence X1, . . . , Xn of independent random variables with distribution P, the random variable n min(X1 , . . . , Xn ) also has distribution P.
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...