Probability- finite n-th moment

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SUMMARY

The discussion centers on proving that a random variable X with a finite exponential moment also possesses finite nth moments for all positive integers n. The key equation referenced is the Taylor series expansion for the exponential function, expressed as E(e^(tX)) = ∑(x^n/(n!), n=0 to ∞). The user seeks assistance in demonstrating this relationship, emphasizing the importance of the proof for an upcoming exam.

PREREQUISITES
  • Understanding of random variables and their moments
  • Familiarity with the concept of exponential moments
  • Knowledge of Taylor series and their applications
  • Basic probability theory and expectations
NEXT STEPS
  • Study the properties of exponential moments in probability theory
  • Learn about the convergence of Taylor series and their implications
  • Explore the relationship between moment generating functions and moments
  • Review examples of finite moments for various probability distributions
USEFUL FOR

Students preparing for exams in probability theory, mathematicians focusing on statistical moments, and educators teaching concepts related to random variables and their properties.

Roni1985
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Homework Statement



Suppose the random variable X has finite exponential moment. Show by comparison to the Taylor series for EXP[x] that X has finite nth moment (E|X|n<inf) for all positive integers n

Homework Equations



ex=[tex]\sum[/tex]([tex]\frac{x^n}{(n!)}[/tex], n,0,inf)

The Attempt at a Solution

we know that E(et*x) < inf

I can use the Taylor expansion but I end up with

E(et*x) < [tex]\sum[/tex]([tex]\frac{x^n}{(n!)}[/tex]

don't know how to pick up from here...

would appreciate any help.

Thanks.
 
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sorry for the bump, it's really important for me :\ have an exam in two days and I've been trying to solve it since 12 pm.

Thanks.
 

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