Moment generating function

In summary, the conversation discusses whether the given equation is correct and states that it will only hold true if the moment generating function is exponential. It also provides an alternative form of the equation and discusses its implications.
  • #1
donutmax
7
0
Is the following correct?

[tex]M(t)=1+t\mu'_1+\frac{t^2}{2!}\mu'_2+\frac{t^3}{3!}\mu'_3+... =\sum_{n=0}^{\infty} \frac {E(Y^n)t^n}{n!}[/tex]

where
[tex]\mu'_n=E(Y^n)[/tex]
 
Last edited:
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  • #2
I think that will only be true if your moment generating function is an exponential - in which case you are just doing a Taylor expansion. This is true for a Wiener process - but I don't think the relation you have holds in general.

Regards,
Thrillhouse86
 
  • #3
[tex]
m_Y(t) = E(e^{ty}) = \int e^{ty} \, dF(y) = \int \sum_{n=0}^\infty \frac{(ty)^n}{n!}\,dF(y) = \sum_{n=0}^\infty \left(\int \frac{(ty)^n}{n!} \, dF(y) \right)
[/tex]

What do you get from working with the final form above?
 

1. What is a moment generating function?

A moment generating function is a mathematical function used in probability theory to generate the moments of a probability distribution. It is defined as the expected value of e^tx, where t is a real-valued parameter and x is a random variable.

2. How is a moment generating function different from a probability density function?

A probability density function describes the probability distribution of a random variable, while a moment generating function is a mathematical function used to generate the moments of that distribution. In other words, a moment generating function is a tool used to analyze and describe a probability distribution, while a probability density function is the actual description of that distribution.

3. What information can be obtained from a moment generating function?

A moment generating function can be used to calculate the moments, or statistical properties, of a probability distribution. This includes the mean, variance, skewness, and kurtosis of the distribution.

4. How is a moment generating function used in hypothesis testing?

In hypothesis testing, a moment generating function is used to generate the probability distribution of a test statistic. This distribution can then be compared to a known distribution to determine the likelihood of obtaining the observed test statistic by chance. This information is used to make a decision about the validity of a hypothesis.

5. Are there any limitations to using a moment generating function?

One limitation of using a moment generating function is that it may not exist for certain probability distributions. In addition, the moment generating function may not always be easy to calculate, especially for complex distributions. Finally, moment generating functions are typically used for continuous random variables, so they may not be applicable to discrete distributions.

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