Mean Squared Error of an estimator.

In summary: Hi. Let X_1 and X_2 be independent random variables withmean μ and variance σ^2.Theta = (X_1+3X_2)/4a) is it unbiased?Yes, the estimator is unbiased.b) what is the variance of the estimator?The variance of the estimator is E[ (X1+3X2/4) - μ]^2 + [(X1+3X2/4) - μ)^2c) what is the mean squared error of the estimator?The mean squared error of the estimator is
  • #1
Ddvon
2
0
Hi.

Let X_1 and X_2 be independent random variables with
mean  μ and variance σ^2.

[itex]\Theta[/itex] = ( X_1 + 3X_2 ) /4

a) is it unbiased?

b) what is the variance of the estimator?

c) what is the mean squared error of the estimator?




since there are four things, divided by 4, it is unbiased.

Then the variance is E[ (X1 + 3X2/4) - μ]^2 + [(X1+3X2/4) - μ)^2

and while expanding this, I got stuck when it was time "get the stuff out of E"

Can anyone help me with this? I have been searching (book has one paragraph long explanation) for many hours, but no avail.

Thank you
 
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  • #2
I am a little confused by your question. I get E(Θ) = μ, Var(Θ) = Var((X_1)/4) + Var(3(X_2)/4) = 5σ2/8.

What is the difference between mean square error and variance - I thought they were the same by definition.
 
  • #3
I thought that

Mean squared error (MSE)

MSE([itex]\Theta[/itex]) = E([itex]\Theta[/itex] - θ)^2

so

MSE([itex]\Theta[/itex]) = V([itex]\Theta[/itex]) + bias^2

isn't it?
 
  • #4
θ Θ - could you define these symbols precisely. What is the definition of bias?
 
  • #5
mathman said:
θ Θ - could you define these symbols precisely. What is the definition of bias?

Bias is a standard definition where bias = E[theta_hat] - theta where theta_hat is an estimator (based on a random sample) for theta.
 
  • #6
Ddvon said:
I thought that

Mean squared error (MSE)

MSE([itex]\Theta[/itex]) = E([itex]\Theta[/itex] - θ)^2

so

MSE([itex]\Theta[/itex]) = V([itex]\Theta[/itex]) + bias^2

isn't it?

That's correct.
 
  • #7
Ddvon said:
Hi.

Let X_1 and X_2 be independent random variables with
mean  μ and variance σ^2.

[itex]\Theta[/itex] = ( X_1 + 3X_2 ) /4

a) is it unbiased?

b) what is the variance of the estimator?

c) what is the mean squared error of the estimator?




since there are four things, divided by 4, it is unbiased.

Then the variance is E[ (X1 + 3X2/4) - μ]^2 + [(X1+3X2/4) - μ)^2

and while expanding this, I got stuck when it was time "get the stuff out of E"

Can anyone help me with this? I have been searching (book has one paragraph long explanation) for many hours, but no avail.

Thank you

Hey Ddvon and welcome to the forums.

What parameter are you trying to estimate? Is it the mean or variance of the distribution? Something else perhaps?
 
  • #8
It would be a more helpful if you described what you are driving at. I think (but I am not sure) you are computing a statistical average and using it to estimate the mean.

In the problem you are posing, what is the average and what is the random variable? I have trouble distinguishing. Θ = ( X_1 + 3X_2 ) /4 is the only thing defined.
 

FAQ: Mean Squared Error of an estimator.

1. What is the Mean Squared Error (MSE) of an estimator?

The Mean Squared Error (MSE) of an estimator is a measure of the average squared difference between the estimated values and the true values. It is a common measure of the accuracy of an estimator and is often used to compare different estimators for the same dataset.

2. How is the MSE calculated?

The MSE is calculated by taking the average of the squared differences between the estimated values and the true values. This means taking the sum of the squared differences and dividing it by the total number of data points.

3. What does a high MSE indicate?

A high MSE indicates that the estimator is not accurate and has a large difference between the estimated values and the true values. It can also indicate that the model used for the estimation is not a good fit for the data.

4. Can the MSE be negative?

No, the MSE cannot be negative. The squared differences used in the calculation ensures that the result will always be a positive value.

5. How can MSE be used in model selection?

MSE can be used as a metric to compare the performance of different models. A lower MSE value indicates a more accurate model, and therefore, it can be used to select the best model for a given dataset.

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