ok, so we know that an estimator for 1/theta, for 0<y<theta is (theta hat) = 2/n * sum from 1 to n of Y(i).(adsbygoogle = window.adsbygoogle || []).push({});

to prove that the estimator is unbiased, I need to show that the expected value of (theta hat) = theta.

so E(2/n*sum from 1 to n of Y(i)) =

2/n * sum from 1 to n of E(Y(i)).

then the book says we can cancel stuff because E(Y(i)) = theta/2.

so why is it equal to theta/2? I'm doing other problems simlar to this, so do I just put E(Y(i)) = theta/2 for everything?

confused...

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# Homework Help: Estimator question

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