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# Convergence in Distribution - PowerPoint PPT Presentation

Convergence in Distribution. Recall: in probability if Definition Let X 1 , X 2 ,…be a sequence of random variables with cumulative distribution functions F 1 , F 2 ,… and let X be a random variable with cdf

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## PowerPoint Slideshow about 'Convergence in Distribution' - mihaly

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Presentation Transcript

• Recall: in probability if

• Definition

Let X1, X2,…be a sequence of random variables with cumulative

distribution functions F1, F2,… and let X be a random variable with cdf

FX(x). We say that the sequence {Xn} converges in distribution to X if

at every point x in which F is continuous.

• This can also be stated as: {Xn} converges in distribution to X if for all such that P(X = x) = 0

• Convergence in distribution is also called “weak convergence”. It is weaker then convergence in probability. We can show that convergence in probability implies convergence in distribution.

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• Assume n is a positive integer. Further, suppose that the probability mass function of Xn is:

Note that this is a valid p.m.f for n ≥ 2.

• For n ≥ 2, {Xn} convergence in distribution to X which has p.m.f

P(X = 0) = P(X = 1) = ½ i.e. X ~ Bernoulli(1/2)

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• X1, X2,…is a sequence of i.i.d random variables with E(Xi) = μ < ∞.

• Let . Then, by the WLLN for any a > 0

as n  ∞.

• So…

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• Let X be a random variable such that for some t0 > 0 we have mX(t) < ∞ for

. Further, if X1, X2,…is a sequence of random variables with

and for all

then {Xn} converges in distribution to X.

• This theorem can also be stated as follows:

Let Fn be a sequence of cdfs with corresponding mgf mn. Let F be a cdf with

mgf m. If mn(t) m(t) for all t in an open interval containing zero, then

Fn(x) F(x) at all continuity points of F.

• Example:

Poisson distribution can be approximated by a Normal distribution for large λ.

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• Let λ1, λ2,…be an increasing sequence with λn∞ as n ∞ and let {Xi} be

a sequence of Poisson random variables with the corresponding parameters.

We know that E(Xn) = λn = V(Xn).

• Let then we have that E(Zn) = 0, V(Zn) = 1.

• We can show that the mgf of Zn is the mgf of a Standard Normal random variable.

• We say that Zn convergence in distribution to Z ~ N(0,1).

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• Suppose X is Poisson(900) random variable. Find P(X > 950).

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• The central limit theorem is concerned with the limiting property of sums of random variables.

• If X1, X2,…is a sequence of i.i.d random variables with mean μ and variance σ2 and ,

then by the WLLN we have that in probability.

• The CLT concerned not just with the fact of convergence but how Sn/n fluctuates around μ.

• Note that E(Sn) = nμ and V(Sn) = nσ2. The standardized version of Sn is

and we have that E(Zn) = 0, V(Zn) = 1.

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• Let X1, X2,…be a sequence of i.i.d random variables with E(Xi) = μ < ∞ and Var(Xi) = σ2 < ∞. Suppose the common distribution function FX(x) and the common moment generating function mX(t) are defined in a neighborhood of 0. Let

Then, for - ∞ < x < ∞

where Ф(x) is the cdf for the standard normal distribution.

• This is equivalent to saying that converges in distribution to

Z ~ N(0,1).

• Also,

i.e. converges in distribution to Z ~ N(0,1).

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• Suppose X1, X2,…are i.i.d random variables and each has the Poisson(3) distribution. So E(Xi) = V(Xi) = 3.

• The CLT says that as n  ∞.

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• A very common application of the CLT is the Normal approximation to the Binomial distribution.

• Suppose X1, X2,…are i.i.d random variables and each has the Bernoulli(p)

distribution. So E(Xi) = p and V(Xi) = p(1- p).

• The CLT says that as n  ∞.

• Let Yn = X1 + … + Xn then Yn has a Binomial(n, p) distribution.

So for large n,

• Suppose we flip a biased coin 1000 times and the probability of heads on any one toss is 0.6. Find the probability of getting at least 550 heads.

• Suppose we toss a coin 100 times and observed 60 heads. Is the coin fair?

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