Homework 4 Practice - Theory of Probability | STAT 831, Assignments of Probability and Statistics

Material Type: Assignment; Class: Theory of Probability; Subject: STATISTICS; University: University of Wisconsin - Madison; Term: Fall 2008;

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Math/Stat 831 Fall 2008
Homework 4
Due: November 13, 2008
1. Show that the difference of two identically distributed independent random variables
cannot have a uniform distribution on [-1,1].
2. Prove that if Xnconverges in distribution to a constant cthen Xn
P
c.
3. Let X1, X2, . . . i.i.d with distribution function F(x). Denote the maximum of the first
nelement by Mn. Show that if
lim
x→∞
xα(1 F(x)) = b
with fixed positive constants α, b then n1Mnconverges in distribution and identify
the limiting distribution.
Hint: you can use the convergence of the distribution functions to prove the weak limit.
4. (Ex. 2.3.13) Use characteristic functions to prove the following identity
sin(t)
t=
Y
k=1
cos µt
2k.
5. Prove the weak law of large numbers using characteristic functions.
Hint: use the result of the Problem 2.
6. (Ex. 2.4.9) Let X1, X2, . . . independent with the following distribution:
P(Xm=m) = P(Xm=m) = 1
2m2,P(Xm= 1) = P(Xm=1) = 1
21
2m2.
Let Sn=X1+· · · +Xn. Show that V ar(Sn)
n2, but Sn
nN(0,1).
Bonus problem
1. Let X1, X2, . . . be i.i.d. Bernoulli(1/2) random variables. Let νndenote the index kwhen
we first have at least nzeros and nones among X1,. . . , Xk. Prove that νn2n
nconverges
in distribution and find the limit.

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Math/Stat 831 – Fall 2008

Homework 4

Due: November 13, 2008

  1. Show that the difference of two identically distributed independent random variables cannot have a uniform distribution on [-1,1].
  2. Prove that if Xn converges in distribution to a constant c then Xn −→P c.
  3. Let X 1 , X 2 ,... i.i.d with distribution function F (x). Denote the maximum of the first n element by Mn. Show that if

xlim→∞ xα(1^ −^ F^ (x)) =^ b

with fixed positive constants α, b then n−^1 /αMn converges in distribution and identify the limiting distribution. Hint: you can use the convergence of the distribution functions to prove the weak limit.

  1. (Ex. 2.3.13) Use characteristic functions to prove the following identity

sin(t) t

∏^ ∞

k=

cos

t 2 k

  1. Prove the weak law of large numbers using characteristic functions. Hint: use the result of the Problem 2.
  2. (Ex. 2.4.9) Let X 1 , X 2 ,... independent with the following distribution:

P(Xm = m) = P(Xm = −m) = 1 2 m^2

, P(Xm = 1) = P(Xm = −1) =^1 2

2 m^2

Let Sn = X 1 + · · · + Xn. Show that V ar n(S n)→ 2, but √Snn ⇒ N (0, 1).

Bonus problem

  1. Let X 1 , X 2 ,... be i.i.d. Bernoulli(1/2) random variables. Let νn denote the index k when we first have at least n zeros and n ones among X 1 ,... , Xk. Prove that νn√−n^2 nconverges in distribution and find the limit.