De Moivre Laplace theorem question

In summary, the de Moivre Laplace theorem states that if p and q are equal to 1/2, then the binomial(n,1/2) distribution can be approximated by the normal(0,1) distribution. Additionally, the Portmanteau theorem states that, if certain properties are met, f(x) can be approximated by f(x)~if~x>m, without needing to know convergence in distribution.
  • #1
noowutah
57
3

Homework Statement



According to the de Moivre Laplace theorem

[tex]\binom{n}{k}p^{k}q^{n-k}\approx\frac{1}{\sqrt{2\pi{}npq}}e^{-\frac{(k-np)^{2}}{2npq}}[/tex]

For p=q=1/2 this translates nicely into an approximation for the binomial distribution by the normal distribution (the +1/2 is a continuity correction):

[tex]\binom{n}{k}\approx{}2^{n}N(\frac{n}{2},\frac{n}{4})(k)[/tex]

and therefore

[tex]\mbox{(A)}\quad \sum_{k=0}^{m}\binom{n}{k}\approx{}2^{n}\int_{-\infty}^{m+\frac{1}{2}}N(\frac{n}{2},\frac{n}{4})(x)dx[/tex]

This appears to be correct. I am trying to solve a problem for which I need

[tex]\sum_{k=0}^{m}k\binom{n}{k}[/tex]

and I am wondering what would keep me from reasoning in analogy to (A) so that

[tex]\mbox{(B)}\quad \sum_{k=0}^{m}k\binom{n}{k}\approx{}2^{n}\int_{-\infty}^{m+\frac{1}{2}}xN(\frac{n}{2},\frac{n}{4})(x)dx[/tex]

Unfortunately, when I use (B) I get some very counter-intuitive results (too complicated to expand on them here).

Homework Equations



see above

The Attempt at a Solution



see above
 
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  • #2
Hi stlukits! :smile:

Let X have the binomial(n,1/2) distribution, and let Z have the normal(0,1) distribution. Let

[tex]Y_n=\frac{X-n/2}{\sqrt{n/4}}[/tex]

What you are using is that Yn converges in distribution to Z. This is correct and yields the good results you're getting.

However, after that you use something like

[tex]E[f(Y_n)]\rightarrow E[f(Z)][/tex]

For

[tex]f(x)=\left\{\begin{array}{c}x~\text{if}~x\leq m\\ 0~\text{if}~x>m\end{array}\right.[/tex]
However, this does not need to hold if you only know convergence in distribution. By Portmanteau theorem, this only holds for functions f with certain properties (like boundedness, which this function f is not).

However, not all is lost. You can do

[tex]\binom{n}{k}=\frac{n}{k}\binom{n-1}{k-1}[/tex]

and thus

[tex]\sum_{k=1}^m{k\binom{n}{k}}=n\sum_{k=1}^m{\binom{n-1}{k-1}}[/tex]

And now you can apply formula A again (which DOES work well!)
 
  • #3
Simple and brilliant. I'll try it. And thanks for explaining why (A) is legit.
 

FAQ: De Moivre Laplace theorem question

What is the De Moivre Laplace theorem?

The De Moivre Laplace theorem, also known as the Central Limit Theorem, is a fundamental result in statistics that states that the sum of a large number of independent and identically distributed random variables will follow a normal distribution.

What is the significance of the De Moivre Laplace theorem?

The De Moivre Laplace theorem allows us to make inferences about a population based on a sample, as long as the sample size is large enough. It also explains why many natural phenomena tend to follow a normal distribution.

How is the De Moivre Laplace theorem used in practice?

The De Moivre Laplace theorem is used in a variety of fields, including finance, economics, and social sciences. It is often used to analyze data and make predictions, as well as to estimate probabilities and confidence intervals.

What are the assumptions of the De Moivre Laplace theorem?

The De Moivre Laplace theorem assumes that the random variables are independent and identically distributed, and that the sample size is large enough (usually at least 30). It also assumes that the variables are normally distributed, or that the sample size is large enough for the Central Limit Theorem to apply.

Are there any limitations to the De Moivre Laplace theorem?

While the De Moivre Laplace theorem is a powerful tool, it does have some limitations. It only applies to independent and identically distributed random variables, and may not be accurate for small sample sizes or in cases where the population does not follow a normal distribution.

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