How can I calculate the moment generating function for moments about the mean?

In summary, the moment generating function (MGF) is a function that can be used to find the central moments of a random variable. To get the central moments about the mean, the MGF can be calculated using the formula E[e^(t(X-m))]=e^(-tm)E[e^(tX)]. This means that the power series expansion is done around the mean, rather than 0. This information can be useful for future reference.
  • #1
shaiguy6
13
0
Hello,

I was wondering if anyone knew how to find a moment generating function about the mean. What I want is a function whose power series expansion gives you a power series where the x^n coefficient is the nth moment about the mean. normally, moment generating functions give you the raw moment (moment about 0). I'd like to convert these to moments about the mean.

Thanks for your help!
 
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  • #2
shaiguy6 said:
Hello,

I was wondering if anyone knew how to find a moment generating function about the mean. What I want is a function whose power series expansion gives you a power series where the x^n coefficient is the nth moment about the mean. normally, moment generating functions give you the raw moment (moment about 0). I'd like to convert these to moments about the mean.

Thanks for your help!

The MGF is E[e^(tX)], so to get the central moments if E[X]=m you calculate E[e^(t(X-m))]=e^(-tm)E[e^(tX)]
 
  • #3
bpet said:
The MGF is E[e^(tX)], so to get the central moments if E[X]=m you calculate E[e^(t(X-m))]=e^(-tm)E[e^(tX)]


Thanks for your help, I don't know how I didn't get this on my own, ugh. Just in case someone uses this thread in the future: You still do the series expansion around 0 (not m) to get moments about a point m.
 

What is a moment generating function?

A moment generating function is a mathematical function that is used to characterize the probability distribution of a random variable. It is defined as the expected value of e^tx, where t is a real number and x is the random variable.

What is the purpose of a moment generating function?

The moment generating function is useful for determining the moments of a probability distribution, such as the mean and variance. It also allows for the calculation of other important parameters, such as skewness and kurtosis. Additionally, it can be used to find the distribution of a sum of independent random variables.

How is a moment generating function different from a characteristic function?

A moment generating function is a real-valued function, whereas a characteristic function is a complex-valued function. Additionally, the moment generating function can only be used for random variables with moments that exist, while the characteristic function can be used for any random variable.

What are the advantages of using moment generating functions?

Moment generating functions have several advantages in statistical analysis. They allow for the determination of moments and other important parameters of a probability distribution, and they can be used to find the distribution of a sum of independent random variables. They also have useful properties, such as the fact that the moment generating function of a sum of independent random variables is equal to the product of their individual moment generating functions.

What are some common applications of moment generating functions?

Moment generating functions are commonly used in statistical analysis and modeling, particularly in the fields of probability theory, statistics, and actuarial science. They are also used in finance and economics, as well as in other fields where the analysis of random variables and probability distributions is important.

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