Dr. Beldi Qiang STATWOB Flotllework #1 1. Let X.,No X~ be a i.İ.d sample form Exp(1),...
(b) For n = 100, give an approximaation for P(Y> 100) (c) Let X be the sample mean, then approximate P(1.1< 1.2) for -100. 2. Consider a random sample XX from CDF F(a) 1-1/ for z [1, 0o) and zero otherwise. (a) Find the limiting distribution of XiI.n, the smallest order statistic. (b) Find the limiting distribution of XI (c) Find the limiting distribution of n In X1:m- 3. Suppose that X,,, are iid. N(0,o2). Find a function of T(x)x...
3. Suppose that X1,X2, ,Xn are i.id. N(0, σ2). Find a function of T(X)-Σǐii verges in distribution to a normal distribution. State the mean and variance of your limiung normal distribution. 4. Stirling's Formula, which gives approximation for factorials, can be derived using CLT. (a) Suppose that X1, X2, random variable Z, .Xn is an ii.d. sample from Exp(1). Show that, for a standard normal PTPZ) (b) Show by differencing both sides of the approximation in part a. Then set...
(a) Suppose that i, X2,... , In is an i.i.d. sample from Exp(1). Show that, for a standard normal random variable Z b) Show Г(n) by differencing both sides of the approximation in part a. Then set a -0 to get Stirling's Formula. 5. Suppose that Y is an id sample from Negative Binomial (n,p). Give a normal approximation of Yn use CLT, when n is large. 6. (Mandatory for Graduate Student. Extra credit for undergrad.) Let Ai, converges to...
tirling's Formula, which gives approximation for factorials, can be derived using CLT. (a) Suppose that X1, X2, random variable Z, , Xn is an ii.d. sample from Exp(1). Show that, for a standard normal (b) Show by differencing both sides of the approximation in part a. Then set 0 to get Stirling's Formula.
1. Let Xi, X2, X, be a 1.1.d. sample form Exp(1), and Y = Σ=i Xi. (a) Use CLT to get a large sample distribution of Y (b) For n = 100, give an approximation for P(Y > 100) (c) Let X be the sample mean, then approximate P(1.1 < X < 1.2) for n = 100.
Let Xi....,Xn,..., ~iid Exp(1) and let Yn) be the sample maximum of the first n observations. Show that the limiting distribution of Zn-(Y(n)-log n) has CDF F(z) exp{-e-*), z є R.
7. Let X1, · · · , Xn be i.i.d. with the density p(x, θ) = θ k (1 − θ) 1−k I{x = 0, 1} (a) Find the ML estimator of θ. (b) Is it unbiased ? (c) Compute its MSE 7. Let Xi, . . . , Xn be i.id, with the density p(z,0)-gk(1-0)1-k1(z-0, 1) (b) Is it unbiased? (c) Compute its MSE 7. Let Xi, . . . , Xn be i.id, with the density p(z,0)-gk(1-0)1-k1(z-0, 1)...
2. Consider a random sample Xi,Xy otherwise. Xu frorn CDF F(x) 1-1/x for x e [1,00) and ze (a) Find the limiting distribution of Xim, the smallest order statistic. (b) Find the limiting distribution of XT (c) Find the limiting distribution of n In X1m
Stirling’s Formula, which gives approximation for factorials, can be derived using CLT. (a) Suppose that X1, X2, · · · , Xn is an i.i.d. sample from Exp(1). Show that, for a standard normal random variable Z, (b) Show by differencing both sides of the approximation in part a. Then set x = 0 to get Stirling’s Formula. We were unable to transcribe this imageГ(n) уж
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.