Mean Squared Error of an estimator.

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Discussion Overview

The discussion revolves around the mean squared error (MSE) of an estimator defined as Θ = (X_1 + 3X_2) / 4, where X_1 and X_2 are independent random variables with a specified mean and variance. Participants explore whether the estimator is unbiased, its variance, and the relationship between variance and mean squared error.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant asserts that the estimator is unbiased because it is divided by 4, suggesting that E(Θ) = μ.
  • Another participant calculates the variance of the estimator as Var(Θ) = Var(X_1)/4 + Var(3X_2)/4, arriving at a value of 5σ²/8.
  • There is a discussion about the definition of mean squared error (MSE), with one participant stating MSE(Θ) = E(Θ - θ)² and relating it to variance and bias.
  • Clarification is sought regarding the symbols used, particularly the distinction between θ and Θ, and the definition of bias.
  • One participant questions the difference between mean squared error and variance, indicating confusion about their definitions.
  • Another participant expresses uncertainty about the parameter being estimated and requests clarification on the statistical average involved in the problem.

Areas of Agreement / Disagreement

Participants express differing views on the definitions and relationships between bias, variance, and mean squared error. There is no consensus on the calculations or interpretations presented, and the discussion remains unresolved.

Contextual Notes

Some participants have not fully defined the parameters involved, leading to ambiguity in the discussion. There are also unresolved mathematical steps regarding the expansion of variance and the relationship between MSE and bias.

Ddvon
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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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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.
 
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?
 
θ Θ - could you define these symbols precisely. What is the definition of bias?
 
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.
 
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.
 
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?
 
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.
 

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