How Do You Calculate Standard Errors for Method of Moments and MLE Estimates?

In summary, the method of moments estimate of y is 5/12 and the MLE is 0.5. To approximate the standard error, you need to adjust for the sample size of 10 and use the formula sqrt(p(1-p)/n), where p is the estimate and n is the sample size. For the method of moments estimate, the approximate standard error is 0.087. For the MLE, the approximate standard error is 0.158.
  • #1
sweetpotatofr
1
0
Hi, I have a question about standard errors in the context of this problem. Any help would be greatly appreciated:

Suppose X is a discrete random variable with

P(X=0) = 2y/3
P(X=1) = y/3
P(X=2) = 2(1-y)/3
P(X=3) = (1-y)/3

Where 0<=y<=1. The following 10 independent observations were taken from such a distribution: (3,0,2,1,3,2,1,0,2,1).

Find the method of moments estimate of y, an approximate standard error for your estimate, the MLE of y, and an approximate standard error of the MLE.
----

I have found the method of moments estimate of y (5/12) and the MLE (.5) but I'm not sure how to go about approximating the standard errors. What I initially did for the SE of the first estimate was to calculate the different y's based on the observed probabilities of the X's, then add the squared differences between them and 5/12, divide by 4, and take the squared root, but that doesn't seem quite right. Sorry to ask such an elementary question, but I'm really puzzled as to how to do this. Thanks in advance!
 
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  • #2
What you are missing is an adjustment for the sample size.
 

Related to How Do You Calculate Standard Errors for Method of Moments and MLE Estimates?

1. What is the purpose of approximating standard errors?

Approximating standard errors is a method used in statistics to estimate the variability or uncertainty in sample data. It allows researchers to make inferences about the population based on the sample data collected.

2. How is standard error different from standard deviation?

Standard error measures the variability of the sample mean, while standard deviation measures the variability of individual data points in a sample. Standard error takes into account the sample size, while standard deviation does not.

3. What are some common methods for approximating standard errors?

Some common methods for approximating standard errors include the standard error formula, the bootstrap method, and the jackknife method. These methods use statistical calculations and resampling techniques to estimate standard errors.

4. Why is it important to consider standard errors in statistical analysis?

Standard errors provide a measure of the precision of our estimates and help us determine the reliability of our results. It is important to consider standard errors in statistical analysis to ensure that our conclusions are not based on chance and to accurately communicate the uncertainty in our findings.

5. How can approximating standard errors be used in hypothesis testing?

In hypothesis testing, standard errors can be used to calculate the test statistic and p-value, which are used to determine the significance of the results. Standard errors can also be used to construct confidence intervals, which can help determine the range of values that the true population parameter is likely to fall within.

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