Finding an Unbiased estimator

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Your Name]In summary, an unbiased estimator for theta with PDF f(x; \theta) = \theta^x (1- \theta) is the sample mean, \hat{\theta} = \frac{1}{n} \Sigma x_i, which has an expected value equal to theta. This can be shown using the linearity property of expectation and the fact that the x values are i.i.d. with expected value \frac{\theta}{1-\theta}.
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Homework Statement



Find an unbiased etimator for theta with PDF [tex]f(x; \theta) = \theta^x (1- \theta)[/tex]. The support of x = 0,1,2,3,...


Homework Equations





The Attempt at a Solution



[tex]E(X) = \Sigma x \theta^x (1- \theta) = (1-\theta)\Sigma x \theta^x = (1-\theta) \frac{\theta}{(1-\theta)^2} = \frac{\theta}{1 - \theta}[/tex].

[tex]E(1+x) = \frac{1}{1- \theta}[/tex]. Now I'm stuck here.

Any help would be greately appreciated.
 
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Hello there,

Thank you for sharing your attempt at the solution. To find an unbiased estimator for theta, we need to find a function of the sample data that gives us an estimate for theta that is not biased, meaning it has an expected value equal to the true value of theta. In this case, we can use the sample mean as an unbiased estimator for theta.

To find the sample mean, we need to calculate the sum of all the observations and divide it by the total number of observations. In this case, the observations are x values, which can take on values of 0, 1, 2, 3, and so on. So, the sample mean can be written as:

\hat{\theta} = \frac{1}{n} \Sigma x_i

where n is the total number of observations and x_i represents each individual observation.

To show that this is an unbiased estimator, we need to find its expected value and show that it is equal to theta. We can do this by using the linearity property of expectation:

E(\hat{\theta}) = E\left(\frac{1}{n} \Sigma x_i\right) = \frac{1}{n} E\left(\Sigma x_i\right) = \frac{1}{n} \Sigma E(x_i)

Now, since all the x values are independent and identically distributed (i.i.d.), we can use the expected value you calculated in your attempt at the solution:

E(x_i) = \frac{\theta}{1-\theta}

So, substituting this into the equation above, we get:

E(\hat{\theta}) = \frac{1}{n} \Sigma \frac{\theta}{1-\theta} = \frac{\theta}{1-\theta} \frac{1}{n} \Sigma 1 = \frac{\theta}{1-\theta}

which is equal to theta, showing that \hat{\theta} is an unbiased estimator for theta.

I hope this helps. Let me know if you have any further questions. Keep up the good work!


 

1. What is an unbiased estimator?

An unbiased estimator is a statistical measure that accurately estimates a population parameter without any systematic over- or underestimation. This means that, on average, the estimator provides an estimate that is equal to the true value of the parameter.

2. Why is it important to have an unbiased estimator?

Having an unbiased estimator is important because it allows researchers to obtain accurate and reliable estimates of population parameters. This is crucial in making informed decisions and drawing valid conclusions from statistical analyses.

3. How can you determine if an estimator is unbiased?

An estimator is considered unbiased if, when taking multiple samples from a population, the average of all the estimates equals the true value of the parameter. This can be tested by calculating the mean of the estimates and comparing it to the true value.

4. What is the difference between a biased and an unbiased estimator?

A biased estimator systematically overestimates or underestimates the true value of a population parameter, while an unbiased estimator provides estimates that are, on average, equal to the true value. Bias can lead to incorrect conclusions and should be minimized or eliminated in statistical analyses.

5. How can you improve the accuracy of an estimator?

There are several ways to improve the accuracy of an estimator. One way is to increase the sample size, as larger samples tend to produce more accurate estimates. Additionally, using more precise measurement techniques and improving the design of the study can also help reduce bias and improve the accuracy of the estimator.

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