What is the method of moment estimator for θ?

In summary, for a random sample from ##f_θ=2x/θ^2##, with ##0≤x≤θ##, the maximum likelihood estimator for ##θ## is the maximum of the sample values. To find the method of moments estimator, we first calculate ##E(X)=2θ/3## and ##E(X^2)=θ^2/2## and then find ##\sum_{i=1}^n x_i / n##, which can be used to estimate the mean of the random variable ##\sum_{i=1}^n X_i /n##.
  • #1
mathmathRW
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0

Homework Statement


Let ##X_1, X_2, ..., X_n## be a random sample from ## f_θ=2x/θ^2## , ##0≤x≤θ##.
Find a maximum likelihood estimator for θ. Find the method of moment estimator for θ.


The Attempt at a Solution


I have already found that the MLE is max{##x_i##}. I just need to find the method of moments estimator. My professor hasn't given any examples on this and everything I have found online seems completely different. I would appreciate some guidance on this one!
 
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  • #2
mathmathRW said:

Homework Statement


Let ##X_1, X_2, ..., X_n## be a random sample from ## f_θ=2x/θ^2## , ##0≤x≤θ##.
Find a maximum likelihood estimator for θ. Find the method of moment estimator for θ.


The Attempt at a Solution


I have already found that the MLE is max{##x_i##}. I just need to find the method of moments estimator. My professor hasn't given any examples on this and everything I have found online seems completely different. I would appreciate some guidance on this one!

What is ##EX_i## in terms of ##\theta##? What is ##E \sum_{i=1}^n X_i / n##? So, what function of ##\theta## is estimated by the mean sample value ##\sum_{i=1}^n x_i / n##?
 
  • #3
Ray Vickson said:
What is ##EX_i## in terms of ##\theta##? What is ##E \sum_{i=1}^n X_i / n##? So, what function of ##\theta## is estimated by the mean sample value ##\sum_{i=1}^n x_i / n##?

I calculated ##E(X)=2θ/3## and ##E(X^2)=θ^2/2##. I am not sure how to find ##\sum_{i=1}^n x_i / n##.
 
  • #4
Ok, I have been looking online some more. Should I find ##σ^2(X)## ? It looks like maybe ##∑X_i^2/n=σ^2+[E(X)]^2##. Is that the method of moments estimator?

I have found ##σ^2(X)=θ^2/18## and ##∑X_i^2/n=σ^2+[E(X)]^2=θ^2/(2n)##.

Am I on the right track?
 
  • #5
mathmathRW said:
I calculated ##E(X)=2θ/3## and ##E(X^2)=θ^2/2##. I am not sure how to find ##\sum_{i=1}^n x_i / n##.

You find ##\sum_{i=1}^n x_i / n## by taking a sample of size n, measuring the resulting ##x_i## values and then computing the sum. On the other hand, the RANDOM VARIABLE ##\sum_{i=1}^n X_i /n## is a different animal completely. As a random variable, it has a certain mean and variance, etc. What are these values, expressed in terms of ##n## and ##\theta## ?
 

FAQ: What is the method of moment estimator for θ?

What is the Method of Moment Estimator?

The Method of Moment Estimator is a statistical technique used to estimate the parameters of a population based on the moments of the data. It is a non-linear method that uses the moments of the data to find the best fit for the model.

How does the Method of Moment Estimator work?

The Method of Moment Estimator works by equating the theoretical moments of a population to the sample moments of the data. This creates a system of equations that can be solved to find the estimated values of the parameters.

What are the advantages of using the Method of Moment Estimator?

The Method of Moment Estimator is easy to use and does not require any assumptions about the underlying distribution of the data. It also provides consistent estimates and has good performance for large sample sizes.

What are the limitations of the Method of Moment Estimator?

The Method of Moment Estimator may produce biased estimates if the underlying distribution of the data is not well represented by the chosen model. It also requires a sufficient number of moments to be known and used in the estimation process.

How is the Method of Moment Estimator different from other estimation methods?

The Method of Moment Estimator differs from other estimation methods in that it does not require knowledge of the entire distribution of the data. It also does not require iterative calculations like maximum likelihood estimation, making it computationally simpler.

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