Unbiased Estimator (15/365)

Posted on September 26, 2016 by Naren Sundar

In the coin tossing setup, we saw that the fraction T_n(\omega) = \frac{S_n(\omega)}{n} of observed heads in n trails \omega approaches p as n \rightarrow \infty. The function T_n is called an estimator that takes on a value in [0,1]. It’s a special kind of estimator called an unbiased estimator because it satisfies

\displaystyle E_\theta T_n = \theta \text{ for all } \theta \in [0,1]

It says that on average, this estimator deduces the correct answer. Consider a different estimator T_n(\omega) = \frac{S_n(\omega)}{n+1} which essentially starts with an assumed ‘tails’. Then

\displaystyle E_\theta T_n = \frac{n}{n+1} E_\theta \frac{S_n}{n} = \frac{n\theta}{n+1}

which, on average, slightly underestimates the success probability.

A problem asks the following. Let it be known a priori that \theta has a value in the set \Omega_0 \subseteq [0,1]. Construct an unbiased estimator for \theta, taking values only in \Omega_0.

Consider the case where \Omega_0 = \{ r \} for r \in [0,1]. Then T_n = r is an unbiased estimator because

\displaystyle E_\theta T_n = r\theta + (1-\theta)r \text{ for } \theta \in \Omega_0

which is unbiased because \theta can only be r. Now, I can’t seem to proceed further than this. For example, what is the estimator when \Omega_0 = \{ r_1, r_2 \}? I’ll have to return with an answer another day.

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