Proof of the Weak Law of Large numbers by using Moment Generating Functions

Your name]In summary, the conversation is about proving the weak law of large numbers using moment generating functions. The user presents their approach and asks for feedback on its correctness. The expert provides corrections and clarifications on the user's approach.
  • #1
stukbv
118
0

Homework Statement



I need a thorough proof of the weak law of large numbers and it must use moment generating functions as below.

Homework Equations



The weak law of large numbers states that given X1...Xn independent and identically distributed random variables with mean μ and variance σ2 then

X = (1/n) * Ʃ Xi tends to μ in distribution as n -> ∞

I am required to start with showing E[eθX ] → eθμ as n→∞

The Attempt at a Solution



This is what I have done.

E[eθX ] = E[eθ*(1/n) * Ʃ Xi ]
E[eθ*(1/n) * Ʃ Xi ] = Product of E[eθ*(1/n)*Xi ] from i = 1 to n

Since the random variables Xi are independent and identically distributed i can just consider the moment generating function of X1,

I know that [itex]\varphi[/itex]x1 (θ/n) = (1 + θμ/n + E[X122/n2)
By the taylor expansion of mgf up to order 1

So now

E[eθ*(1/n) * Ʃ Xi ] = (1 + θμ/n + E[X122/n2)n

And so Log( E[eθ*(1/n) * Ʃ Xi ]) = nLog((1 + θμ/n + E[X122/n2))
= n(θμ/n + E[X122/n2)

By using Log(1+x) = x-x2/2 ...

= θμ + (E[X122/n ) → θμ as n→∞


Is this correct?
 
Physics news on Phys.org
  • #2


Thank you for your question. Your approach to proving the weak law of large numbers using moment generating functions is a valid one. However, there are a few corrections and clarifications that I would like to make.

Firstly, when you write "Product of E[eθ*(1/n)*Xi ] from i = 1 to n," it should actually be the product of the moment generating functions, i.e. ΦXi (θ/n), not just the expectation values.

Secondly, the statement "By the taylor expansion of mgf up to order 1" is slightly misleading. The Taylor expansion of the moment generating function is up to infinite order, not just order 1. However, in this case, we only need to consider up to order 1 for our proof.

Thirdly, when you write "And so Log( E[eθ*(1/n) * Ʃ Xi ]) = nLog((1 + θμ/n + E[X12]θ2/n2))," it should be Log(ΦX (θ/n)), not Log(E[eθ*(1/n) * Ʃ Xi ]).

Lastly, your final expression should be θμ + (E[X12]θ2/n2), not just θμ, as you have correctly stated in the previous line.

Overall, your proof is on the right track, but there are some minor errors and clarifications that need to be made. I hope this helps and please let me know if you have any further questions.
 

FAQ: Proof of the Weak Law of Large numbers by using Moment Generating Functions

1. What is the Weak Law of Large Numbers?

The Weak Law of Large Numbers is a fundamental theorem in probability theory that states that as the number of observations in a sample increases, the sample mean will converge to the population mean.

2. What is a Moment Generating Function?

A Moment Generating Function (MGF) is a mathematical function that uniquely defines a probability distribution. It is defined as the expected value of etx, where t is the variable and x is a random variable.

3. How does the MGF prove the Weak Law of Large Numbers?

By using the MGF, we can show that as the sample size increases, the MGF of the sample mean converges to the MGF of the population mean. This in turn, using the properties of MGFs, proves that the sample mean converges to the population mean.

4. Can the Weak Law of Large Numbers be extended to other distributions?

Yes, the Weak Law of Large Numbers can be extended to other distributions as long as they have finite means and variances. However, the proof using MGFs may differ depending on the distribution.

5. What are the practical applications of the Weak Law of Large Numbers?

The Weak Law of Large Numbers has applications in various fields such as statistics, finance, and engineering. It is used to make predictions and estimate parameters of a population based on a sample. It is also used in quality control to ensure the stability and consistency of a process.

Back
Top