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A single observation is made from a poisson distribution with unknown mean $\lambda \geq 0$ However any value greater than 2 has been rounded down to 2. This we have the observed value of a single random variable X having distribution depending on $\lambda $ given by; $\ P(X=0) = e^{-\lambda}. P(X=1) = \lambda e^{-\lambda} P(X=2) = 1 - (1+\lambda)e^{-\lambda}$
Parameterise the distribution by $\ \theta = e^{-\lambda} \in (0,1] $ Show that there is a unique unbiased estimator of $\theta$.

So I parameterise it; $\ P(X=0) = \theta$ $\ P(X=1) = -\theta log\theta$ $\ P(X=2) = 1-(1-log\theta)\theta$

But I have no idea how to show there is a unique unbiased estimator. Also this is not a homework question, it is a practice paper question.

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    Just a suggestion, I haven't checked if it works: $\theta$ is a function of $X$. Let $\hat \theta$ and $\tilde \theta$ be two estimators of $\theta$, and enumerate $X$ ($\hat \theta$ and $\tilde \theta$ have unique values for all 3 possible X).2012-05-27
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    Oops, what I meant, of course, was that any /estimator/ of $\theta$ is a function of $X$.2012-05-27

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