Central limit theorem - finding cumulants

In summary, the conversation discusses the derivation of the cumulants of a given function y, which is a linear combination of independent variables x. The generating function and cumulant generating function are introduced and used to show that the cumulants of y are related to the cumulants of x, with a factor of N for higher order cumulants. The steps for deriving the cumulant generating function are shown, but the solution is not fully completed.
  • #1
LmdL
73
1

Homework Statement


Given:
[tex]y=\frac{\sum_{i}x_i-N\left \langle x \right \rangle}{\sqrt{N}}[/tex]

Show that the cumulants of y are:
[tex]
\begin{matrix}
\left \langle y \right \rangle_c=0& & \left \langle y^2 \right \rangle_c=\left \langle x^2 \right \rangle_c & & \left \langle y^m \right \rangle_c=\left \langle x^m \right \rangle_c N^{1-m/2}\begin{matrix}
& for & m>2
\end{matrix}
\end{matrix}
[/tex]

Homework Equations


Generating function:
[tex]\tilde{p}\left ( k \right )=\sum_{n=0}^{\infty }\frac{\left ( -ik \right )^n}{n!}\left \langle x^n \right \rangle[/tex]
Cumulant generating function:
[tex]ln \left ( \tilde{p}\left ( k \right ) \right )=\sum_{n=0}^{\infty }\frac{\left ( -ik \right )^n}{n!}\left \langle x^n \right \rangle_c[/tex]

For independent
[tex]X=\left ( x_1,x_2,...,x_N \right )[/tex]
if
[tex]y=a_0+a_1x_1+a_2x_2+...+a_Nx_N[/tex]
then:
[tex]\tilde{p}_y\left ( k \right )=e^{-ika_0}\prod_{i}\tilde{p}_i\left ( a_ik \right )[/tex]

The Attempt at a Solution


In my case:
[tex]y=\frac{\sum_{i}x_i-N\left \langle x \right \rangle}{\sqrt{N}}=-\frac{N\left \langle x \right \rangle}{\sqrt{N}}+\frac{1}{\sqrt{N}}x_1+...+\frac{1}{\sqrt{N}}x_N[/tex]
that is:
[tex]\begin{matrix} a_0=-\frac{N\left \langle x \right \rangle}{\sqrt{N}} & & a_1=...=a_N=\frac{1}{\sqrt{N}}
\end{matrix}[/tex]
Substituting into
[tex]\tilde{p}_y\left ( k \right )=e^{-ika_0}\prod_{i}\tilde{p}_i\left ( a_ik \right )[/tex]
gives:
[tex]\tilde{p}_y\left ( k \right )=e^{\frac{ikN\left \langle x \right \rangle}{\sqrt{N}}}\prod_{i}\tilde{p}_i\left ( \frac{1}{\sqrt{N}}k \right )[/tex]
Cumulant generating function:
[tex]ln \left ( \tilde{p}_y\left ( k \right ) \right )=\frac{ikN\left \langle x \right \rangle}{\sqrt{N}}+\sum_{i}ln\left ( \tilde{p}_i\left ( \frac{1}{\sqrt{N}}k \right ) \right )[/tex]
Next step I tried is to represent ln function as series:
[tex]\sum_{n=0}^{\infty }\frac{\left ( -ik \right )^n}{n!}\left \langle y^n \right \rangle_c = \frac{ikN\left \langle x \right \rangle}{\sqrt{N}}+\sum_{i}^{N}\sum_{n=0}^{\infty }\frac{\left ( -i\frac{1}{\sqrt{N}}k \right )^n}{n!}\left \langle x^n \right \rangle_c=\frac{ikN\left \langle x \right \rangle}{\sqrt{N}}+N\sum_{n=0}^{\infty }\frac{\left ( -i\frac{1}{\sqrt{N}}k \right )^n}{n!}\left \langle x^n \right \rangle_c[/tex]
And, finally, here I'm stuck. Can someone help me how to continue from here?
Thanks in advance.
 
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  • #2
Doesn't matter. Already solved it.
 

Related to Central limit theorem - finding cumulants

1. What is the Central Limit Theorem?

The Central Limit Theorem is a fundamental concept in statistics that states that the sum of a large number of independent random variables will tend towards a normal distribution, regardless of the underlying distribution of the individual variables.

2. How does the Central Limit Theorem apply to finding cumulants?

The Central Limit Theorem provides a way to approximate the distribution of a sum of random variables by using the normal distribution. This makes it possible to calculate the moments and cumulants of the sum by using the moments and cumulants of the individual variables.

3. What are cumulants?

Cumulants are statistical measures that describe the shape, dispersion, and skewness of a probability distribution. They are derived from the moments of the distribution and are useful for summarizing and comparing different distributions.

4. How do you use the Central Limit Theorem to find cumulants?

To use the Central Limit Theorem to find cumulants, you first need to calculate the moments and cumulants of the individual random variables. Then, you can use the formulas for the moments and cumulants of a normal distribution to approximate the moments and cumulants of the sum of the variables.

5. What are some applications of the Central Limit Theorem in real-world situations?

The Central Limit Theorem has many practical applications, such as in finance, quality control, and market research. It is used to model and analyze data and make predictions about future events. For example, it is often used to analyze stock market returns or to estimate the average weight of a population based on a sample size.

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