Finding Method of Moments Estimates for Uniform Distribution Parameters

In summary, the problem involves finding the method of moments estimates for $\alpha$ and $\beta$ using five numbers from a uniform distribution. The first moment is the mean, the 2nd moment is the variance, and the 3rd moment is the skewness. The textbook suggests equating the theoretical moments with the corresponding sample moments to find the estimates.
  • #1
das1
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The problem:

Let the five numbers 2,3,5,9,10 come from the uniform distribution on [$\alpha$,$\beta$]. Find the method of moments estimates for $\alpha$ and $\beta$ .

I am trying to wrap my head around the idea behind estimates of moments. From what I understand, the first moment is the mean, the 2nd moment is variance and the 3rd is the skewness. I want to find the most likely values of both $\alpha$ and $\beta$ , of which the above 5 numbers are between.
The textbook says to equate each theoretical moment with each corresponding sample moment. Not sure how to do this. Can someone help me get started?

Thanks
 
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  • #2
das said:
The problem:

Let the five numbers 2,3,5,9,10 come from the uniform distribution on [$\alpha$,$\beta$]. Find the method of moments estimates for $\alpha$ and $\beta$ .

I am trying to wrap my head around the idea behind estimates of moments. From what I understand, the first moment is the mean, the 2nd moment is variance and the 3rd is the skewness. I want to find the most likely values of both $\alpha$ and $\beta$ , of which the above 5 numbers are between.
The textbook says to equate each theoretical moment with each corresponding sample moment. Not sure how to do this. Can someone help me get started?

Thanks

Hi das,

Take a look at this section of the relevant wiki page.
It gives the formula for the estimates for $\alpha$ and $\beta$.
Can you apply it? (Wondering)
 
  • #3
OK thank you I'll try this
 

Related to Finding Method of Moments Estimates for Uniform Distribution Parameters

1. What is the "Method of Moments" in statistics?

The Method of Moments is a statistical technique used to estimate the parameters of a probability distribution by equating the theoretical moments of the distribution to the empirical moments of a sample.

2. How is the "Method of Moments" different from other estimation methods?

The Method of Moments differs from other estimation methods, such as maximum likelihood estimation, in that it does not require the assumption of a specific probability distribution. Instead, it uses the moments of the data to estimate the parameters of the distribution.

3. What are the steps involved in using the "Method of Moments"?

The steps involved in using the Method of Moments are: 1. Choose a probability distribution to model the data.2. Calculate the theoretical moments of the chosen distribution.3. Calculate the empirical moments of the sample data.4. Equate the theoretical and empirical moments to solve for the parameters of the distribution.5. Use the estimated parameters to make inferences about the population.

4. What are the advantages of using the "Method of Moments"?

One advantage of using the Method of Moments is that it is a relatively simple and straightforward technique, making it accessible to researchers and practitioners with basic statistical knowledge. Additionally, it does not require large sample sizes and can be applied to a wide range of probability distributions.

5. What are the limitations of the "Method of Moments"?

One limitation of the Method of Moments is that it relies on the assumption that the chosen probability distribution accurately represents the data. If the distribution is not a good fit, the estimated parameters may be biased. Additionally, it may not perform well with small sample sizes or with distributions that have heavy tails or extreme outliers.

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