Local Limit Theorem: Explaining Little o Notation

In summary: Basically, they are saying that any real number that is o(npq)^{1/6} is little o of a FIXED real number. If x is any real number, then x is little o of a FIXED real number. Basically, this theorem states that for all real numbers there is a number that is little o of a FIXED real number.
  • #1
ehrenfest
2,020
1

Homework Statement


This theorem is from Shiryaev. Can someone PLEASE explain how they are using the little o notation here. It makes no sense to me how they say that a FIXED real number is little o of something. I thought f(n) = o(g(n)) mean that for ever c>0 there exists an n_o such that when n => n_0, |f(n)|<c|g(n)|.

Homework Equations





The Attempt at a Solution

 

Attachments

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  • #4
ehrenfest said:
It makes no sense to me how they say that a FIXED real number is little o of something.
Where did they say that?
 
  • #5
Hurkyl said:
Where did they say that?

It says for all [tex]x \in R^1[/tex]
 
  • #6
ehrenfest said:
It says for all [tex]x \in R^1[/tex]
That doesn't sound like a FIXED real number to me. And besides, they clarify what they mean immediately afterwards.
 
Last edited:
  • #7
Hurkyl said:
That doesn't sound like a FIXED real number to me.

They say for all [tex]x \in R^1[/tex] such that [tex]x = o(npq)^{1/6}[/tex]. To me that means that you can take any real number that you want and see if it is [tex]o(npq)^{1/6}[/tex]. I don't know how else to interpret it.
 
  • #8
ehrenfest said:
I don't know how else to interpret it.
How about exactly how they interpreted it in the attachment you posted?
 
  • #9
Hurkyl said:
How about exactly how they interpreted it in the attachment you posted?

That is precisely my question: How did they interpret it?
 
  • #10
ehrenfest said:
How did they interpret it?
as [itex]n \rightarrow \infty[/itex],
[tex]
\sup_{\left\{ x : |x| \leq \psi(n) \right\}}
\left|
\frac{P_n(np + x \sqrt{npq})}{
\frac{1}{\sqrt{2\pi npq}} e^{-x^2 / 2}
} - 1
\right| \rightarrow 0,
[/tex]​
where [itex]\psi(n) = o(npq)^{1/6}[/itex].
 
  • #11
Hurkyl said:
as [itex]n \rightarrow \infty[/itex],
[tex]
\sup_{\left\{ x : |x| \leq \psi(n) \right\}}
\left|
\frac{P_n(np + x \sqrt{npq})}{
\frac{1}{\sqrt{2\pi npq}} e^{-x^2 / 2}
} - 1
\right| \rightarrow 0,
[/tex]​
where [itex]\psi(n) = o(npq)^{1/6}[/itex].

OK. I will have to think about that. I am kind of confused about the sup above. Is \psi(n) fixed? If not, I don't understand how the sup is taken. Is it taken over all x AND over all \psi(n) that are [tex]o(npq)^{1/6}[/tex]?
 
  • #12
anyone?
 
  • #13
I FINALLY figured this out! Hurky! was right that they clarify what they mean-that just wasn't clicking for me.
 

1. What is the Local Limit Theorem?

The Local Limit Theorem is a mathematical concept that explains how the sum of a large number of independent, identically distributed random variables tends towards a normal distribution.

2. How does the Local Limit Theorem relate to Little o Notation?

The Local Limit Theorem uses Little o Notation to express the rate at which the sum of random variables approaches the normal distribution. Little o Notation is used to describe the difference between two functions as one of the functions approaches infinity.

3. Why is the Local Limit Theorem important?

The Local Limit Theorem is important in probability theory and statistics because it allows us to make predictions about the behavior of a large number of random variables. It also helps us to understand the properties of the normal distribution, which is a fundamental concept in many areas of science and engineering.

4. What are the assumptions of the Local Limit Theorem?

The Local Limit Theorem assumes that the random variables are independent, identically distributed, and have a finite mean and variance. It also assumes that the sample size is sufficiently large.

5. Can the Local Limit Theorem be applied to any type of random variable?

No, the Local Limit Theorem is only applicable to random variables that are independent and identically distributed. It also requires that the sample size is sufficiently large and that the random variables have a finite mean and variance.

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