Chapter 5 Limit Theorems - SDSU Library

[Pages:31]Chapter 5 Limit Theorems

5.2 The Law of Large Numbers (Convergence in Probability)

(2 Examples from last lecture)

Example Let X(n) denote the nth order statistic of a random sample X1, X2, ..., Xn from a distribution having pdf

1 f (x) =

for 0 < x < , 0 < < .

Show that X(n) converges in probability to . Recall from last lect ure, we found that for 0 < x < ,

n

Fn(x) = P [X(n) x] = P [Xi x]

i=1

Now, let 0 < < .

n

=

x 1du = x n

i=1 0

- n P [|X(n) - | > ] = P [X(n) < - ] = which approaches 0 as n approaches infinity.

Example: Let X(n) denote the nth order statistic of a sample of size n from a

uniform distribution on the interval (0, ).

Show that Zn =

X(n) converges in

probability to .

We know from previous example, that X(n) converges in probability to . Also, we know that g(x) = x is a continuous function on the nonnegative real numbers. So, the fact that Zn converges in probability to follows from your Homework Problem.

Suppose we didn't have that general result of applying continuous transformat ion. How would we solve this problem directly?

In that case, we would need to show that for any > 0,

However, notice that

nlim P [| - X(n)| > ] = 0

P [| - X(n)| > ] = P [ - X(n) > ]

= P [( - X(n))( + X(n)) > ( + X(n))]

P [ - X(n) > ]

which we know converges to 0 from previous Example.

5.3 Convergence in Distribution and the Central Limit Theorem

We have seen examples of random variables that are created by applying a function to the observations of a random sample. The distribution of such a statistic often depends on n, the size of the sample.

For example, let X? denote the sample mean of a random sample X1, .X2, ..., Xn from the distribution N (?, 2).

We have seen that X? is distributed N (?, 2/n).

Thus, the distribution of X? depends on n. In some cases we might wish to denote X? by X?n, to emphasize the dependence of the distribution on the size of the sample.

Example 1: Consider a random sample X1, X2, ..., Xn where each Xi has distribution F (x).

Let X(n) = max{X1, X2, ..., Xn}. X(n) is the nth order statistic in a sample of size n. We have seen that the distribution function for X(n) is

Fn(x) = [F (x)]n

(In general) Let Xn denote a random variable whose distribution function Fn(x) depends on n for n = 1, 2, 3, .... In many cases, we are interested in knowing if Fn(x) converges to some fixed distribution function F (x).

Definition Let X1, X2, . . . be a sequence of random variables with cummulative distributin functions F1, F2, . . ., and let X be a random variable with distribution function F . We say that Xn converges in distribtuion to X if

nlim Fn(x) = F (x)

at every point x at which F (x) is continuous.

First, let's recall the definition of continuity, and the meaning of a limit.

An infinite sequence of real numbers a1, a2, a3, ... has limit a (converges to a), if for any number > 0 there is an integer n0 such that for any n > n0

|a - an| <

Then we can say

nlim an = a

For

example,

consider

a

sequence

where

an

=

1

+

1 n

.

Show that the limit of the

sequence is the number 1.

Let > 0 and select n0 to be the smallest integer greater than 1/ . Then for any n > n0,

1 |1 - an| = n <

Using this notion, we say that the function F (y) is continuous at y if for any sequence y1, y2, .. such that

nlim yn = y we also have

nlim F (yn) = F (y).

Example 2 Let X(n) denote the nth order statistic of a random sample X1, X2, ..., Xn from a distribution having pdf

1 f (x) =

for 0 < x < , 0 < < .

Clearly Fn(x) = 0 for x < 0 and Fn(x) = 1 for x < , and for 0 x < ,

n

Fn(x) = P [X(n) x] = P [Xi x]

i=1

n

=

x

1 du

=

xn

i=1 0

n

We see that

limn Fn(x) = 0 for - < x < and limn Fn(x) = 1 for x <

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