Expected value nd variance of mean estimator

In summary, the estimator \hat{\overline{Y}} is based on the mean of n1 units and the sub-sample of size n_{1} units. The estimator has variance w.
  • #1
safina
28
0

Homework Statement


A sample of size n is drawn from a population having N units by simple random sampling without replacement. A sub-sample of size [tex]n_{1}[/tex] units is drawn from the n units by simple random sampling without replacement. Let [tex]\bar{y_{1}}[/tex] denote the mean based on [tex]n_{1}[/tex] units and [tex]\bar{y_{2}}[/tex] based on (n-[tex]n_{1}[/tex]) units.
Consider the estimator [tex]\hat{\overline{Y}}[/tex] = w[tex]\bar{y_{1}}[/tex] + (1-w)[tex]\bar{y_{2}}[/tex].
Show that E[[tex]\hat{\overline{Y}}[/tex]] =[tex]\overline{Y}[/tex] and obtain its variance.

Homework Equations



The Attempt at a Solution


E[[tex]\hat{\overline{Y}}[/tex]] = E[w[tex]\bar{y_{1}}[/tex] + (1-w)[tex]\bar{y_{2}}[/tex]]
= w E[[tex]\bar{y_{1}}[/tex]] + (1-w) E[[tex]\bar{y_{2}}[/tex]]
= w[tex]\overline{Y}_{1}[/tex] + (1-w)[tex]\overline{Y}_{2}[/tex]
Why I did not arrive at the rigth answer which is [tex]\overline{Y}[/tex]?
 
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  • #2
What exactly do you mean by [tex]\bar{Y}[/tex], [tex]\bar{Y}_1[/tex], and [tex]\bar{Y}_2[/tex]?
 
  • #3
vela said:
What exactly do you mean by [tex]\bar{Y}[/tex], [tex]\bar{Y}_1[/tex], and [tex]\bar{Y}_2[/tex]?

They are not stated in the problem, but I think [tex]\bar{Y}[/tex] is the overall mean, [tex]\bar{Y}_1[/tex] is the mean of n1 units, and [tex]\bar{Y}_2[/tex]?[/QUOTE] is the mean of the remaining (n-n1) units
 
  • #4
Then what's the difference between [tex]\bar{y}_1[/tex] and [tex]\bar{Y}_1[/tex]?
 
  • #5
vela said:
Then what's the difference between [tex]\bar{y}_1[/tex] and [tex]\bar{Y}_1[/tex]?

I am sorry, I mean [tex]\bar{y}_1[/tex] is the mean of the n1 units and [tex]\bar{y}_2[/tex] is the mean of the remaining (n-n1) units.
 
  • #6
OK, let's try this instead. You have

[tex]\bar{y}_1 = \frac{1}{n_1}\sum_{i=1}^{n_1} y_i[/tex]

So evaluate its expected value:

[tex]E[\bar{y}_1] = E\left[\frac{1}{n_1}\sum_{i=1}^{n_1} y_i\right] = \cdots\,?[/tex]

What do you get?
 
  • #7
Ok, I figured them out.

[tex]E\left[\widehat{\bar{Y}}\right]=E\left[w\bar{y_{1}}+(1-w)\bar{y_{2}}\right][/tex]
[tex]=wE\left[\bar{y_{1}}\right]+(1-w)E\left[\bar{y_{2}}\right][/tex]
[tex]=\left(\frac{n_{1}}{n}\right)[/tex] E[tex]\left[\frac{1}{n_{1}}\sum^{n_{1}}_{1}y_{i}\right][/tex] +[tex]\left(\frac{n-n_{1}}{n}\right)[/tex]E[tex]\left[\frac{1}{n-n_{1}}\sum^{n}_{n_{1}+1}y_{i}\right][/tex]
=[tex]\frac{1}{n}\left[E\left\{\sum^{n_{1}}_{1}y_{i}+\sum^{n}_{n_{1}+1}y_{i}\right\}\right][/tex]
[tex]=\frac{1}{n}\sum^{n}_{1}E\left[y_{i}\right][/tex]
[tex]=\frac{1}{n}\sum^{n}_{1}\overline{Y}[/tex]
[tex]=\overline{Y}[/tex]

Thank you so much Vela!
 

What is the expected value of a mean estimator?

The expected value of a mean estimator is the average value that we expect the estimator to produce over multiple trials. It is a measure of the central tendency of the estimator.

How is the expected value of a mean estimator calculated?

The expected value of a mean estimator is calculated by taking the sum of all possible values of the estimator multiplied by their respective probabilities. This is also known as the weighted average of the estimator's values.

What does the expected value of a mean estimator tell us?

The expected value of a mean estimator tells us the average value that the estimator will produce, which can be useful in predicting the performance of the estimator and making decisions based on its results.

What is the variance of a mean estimator?

The variance of a mean estimator is a measure of how spread out the values of the estimator are from its expected value. It is a measure of the variability or uncertainty of the estimator.

How is the variance of a mean estimator calculated?

The variance of a mean estimator is calculated by taking the average of the squared differences between each value of the estimator and its expected value. This gives us a measure of the spread or dispersion of the estimator's values.

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