Estimator Exercise Homework Solution

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    Exercise
In summary, the probability of success is ##X/n##. The standard error of the probability of success is ##\sigma_{\hat{P}}##. The moment estimator for the probability of success is ##\hat{\Theta}=3\bar{X}##.
  • #1
archaic
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Homework Statement:: 1) ##X## is number of success out of ##n## trials where ##p## is the probability of success.
1a) Show that ##\mathrm{E}\left[\hat{P}\right]-p=0##, where ##\hat{P}=X/n##.
1b) Find the standard error of ##\hat{P}##, then give calculate the estimated standard error if there are ##5## successes out of ##10## trials.

2) Consider the probability density function ##f(x)=0.5(1+\Theta x)## defined on ##[-1,1]##.
Find the moment estimator for ##\Theta##, then show that ##\hat{\Theta}=3\bar{X}## is an unbiased estimator.
Relevant Equations:: N/A

1a) ##\mathrm E\left[\hat P\right]-p=\mathrm E\left[X/n\right]-p=\frac1n\mathrm E\left[X\right]-p=\frac1nnp-p=0##.
1b)$$\begin{align*}
\mathrm E\left[{\left(\hat{P}-\mathrm E\left[{\hat{P}}\right]\right)^2}\right]&=\mathrm E\left[{\left(\frac{X}{n}-\mathrm E\left[{\frac Xn}\right]\right)^2}\right]\\
&=\mathrm E\left[{\left(\frac Xn-p\right)^2}\right]\\
&=\mathrm E\left[{\frac{\left(X-np\right)^2}{n^2}}\right]\\
&=\frac{1}{n^2}\mathrm E\left[{\left(X-np\right)^2}\right]\\
&=\frac{1}{n^2}\mathrm E\left[{\left(X-\mathrm E\left[{X}\right]\right)^2}\right]\\
&=\frac{p\left(1-p\right)}{n}\\
\sigma_{\hat{P}}&=\sqrt{\frac{p\left(1-p\right)}{n}}
\end{align*}$$
With ##\hat P=5/10=0.5##, I get ##\hat{\sigma}_{\hat P}=\frac{0.5(1-0.5)}{10}=\frac{\sqrt{10}}{20}##.

2)
Since we only have one parameter to estimate, we use ##\mathrm E\left[{X}\right]##.
$$\begin{align*}
\mathrm E\left[{X}\right]&=\int_{-1}^1x(1+\Theta x)\,dx\\
&=\frac12\int_{-1}^1x\,dx+\frac12\Theta\int_{-1}^1x^2\,dx\\
&=\frac13\Theta
\end{align*}$$The moment estimator is ##\hat{\Theta}=3\bar{X}##.$$\begin{align*}
\mathrm E\left[\hat{\Theta}\right]-\Theta&=3\mathrm E\left[{\bar{X}}\right]-\Theta\\
&=3\times\frac{\Theta}{3}-\Theta\\
&=0
\end{align*}$$
 
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  • #2
Not a probability guy, but this shouldn't be in precalc
 
  • #3
Moved to statistics.
 
  • #4
Everything looks fine to me, but I'm not confident the best estimate of the stdev is plugging in your estimate for p to that formula. I don't have any specific reason to think that is the wrong thing to do, it just feels like the type of calculation where there might be some subtly better thing to do.
 
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  • #5
Office_Shredder said:
Everything looks fine to me, but I'm not confident the best estimate of the stdev is plugging in your estimate for p to that formula. I don't have any specific reason to think that is the wrong thing to do, it just feels like the type of calculation where there might be some subtly better thing to do.
well, i submitted it anyhow. :oldshy:
jim mcnamara said:
Moved to statistics.
hm, so we can post homework in here?
 
  • #6
I think this should have been moved to the calculus homework section.
 
  • #7
Office_Shredder said:
I think this should have been moved to the calculus homework section.
Done. :smile:
 
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  • #8
archaic said:
## E[X]=\int_{-1}^1x(1+\Theta x)\,dx\\
=\frac12\int_{-1}^1x\,dx+\frac12\Theta\int_{-1}^1x^2\,dx\\##

Are you thinking that ##E( g(x) + h(x)) = (1/2) E(g(x)) + (1/2) E(h(x))##?
That isn't true.
 
  • #9
Stephen Tashi said:
Are you thinking that ##E( g(x) + h(x)) = (1/2) E(g(x)) + (1/2) E(h(x))##?
That isn't true.
Comes from his definition of the density formula, which contains a 0.5 term.
 
  • #10
Yeah there's a 1/2 missing in the first line of his equation series.
 
  • #11
Stephen Tashi said:
Are you thinking that ##E( g(x) + h(x)) = (1/2) E(g(x)) + (1/2) E(h(x))##?
That isn't true.
Sorry about that! I forgot the ##0.5## of the density function.
 
  • #12
Now that I look at the variance equation... I could've directly factorized ##\frac1n##. :nb)
 

1. What is an estimator exercise?

An estimator exercise is a statistical problem or scenario that requires the use of estimation techniques to find a solution. This type of exercise typically involves using data to make predictions or draw conclusions about a population or process.

2. What is the purpose of an estimator exercise?

The purpose of an estimator exercise is to develop and hone skills in using estimation techniques to solve statistical problems. This type of exercise also helps to improve critical thinking and problem-solving skills.

3. What are some common estimation techniques used in these exercises?

Some common estimation techniques used in these exercises include point estimation, interval estimation, and maximum likelihood estimation. Other techniques may include regression analysis, hypothesis testing, and confidence intervals.

4. How do I know if my solution to an estimator exercise is accurate?

The accuracy of your solution to an estimator exercise can be evaluated by comparing it to the true value or known solution, if available. Additionally, you can use statistical measures such as mean absolute error or root mean squared error to assess the accuracy of your solution.

5. Can an estimator exercise be solved using only one method?

No, an estimator exercise may require the use of multiple methods or techniques to arrive at a solution. It is important to understand the strengths and limitations of each method and choose the most appropriate one for the given problem.

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