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http://mathoverflow.net/questions/14964/estimate-population-size-based-on-repeated-observation asks the following question.

I take the bus to work every day. Every bus has a serial number, but unlike in the German Tank Problem, I don't know if they are numbered uniformly $1...n$.

Suppose the first $k$ buses are all different, but on day $k+1$ I take one I've been on before. What is the best estimate for the total number of buses?

The provided answer gives a maximum likelihood estimator as well as an unbiased estimator of $k(k+1)/2$

If you know the number of buses can't be larger than some given value $N \geq k+1$, how does that change the maximum likelihood estimator and the unbiased estimator ?

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The unbiased estimator doesn't change. The requirement that the expected value of the estimate is $n$ for all $N$ possible values of $n$ yields $N$ linear contraints on the $N$ estimates of $n$ for the $N$ different possible values of $k$. This $N\times N$ system of linear equations is triangular with non-zero diagonal and thus has a unique solution; since we know that $k(k+1)/2$ is a solution, this is the only unbiased estimator. It yields estimates of $n$ greater than $N$ for most values of $k$, but there is no unbiased estimator without this undesirable property. This is also intuitively clear, since for $n=N$ the estimates below $N$ must be compensated by estimates above $N$ to obtain the expected value $N$.

The maximum likelihood estimator changes in that if the original maximum likelihood estimate would have been greater than $N$, this should be replaced by $N$, since the likelihood is unimodal and thus takes its maximum on $\{1,\dotsc,N\}$ at $N$ if the maximum likelihood estimate is greater than $N$.

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so the unbiased estimator seems like a terrible idea in this case. Is there a better choice? –  Anush Dec 6 '12 at 9:01
    
@Anush: It's not as terrible as it may seem, since estimates above $N$ are quite unlikely unless $n$ is close to $N$. If $n$ is close to $N$, there's not much you can do; e.g. for $n=N$, either you accept some estimates above $N$ or you get a seriously biased estimator. As always in this area, "a better choice" depends on your criteria for an estimator. Certainly the maximum likelihood estimator is a possible choice that avoids the problem of estimates greater than $N$. –  joriki Dec 6 '12 at 9:10
    
Thank you very much. I am also interested in a Bayesian approach but that is a separate question I know. –  Anush Dec 6 '12 at 9:24
    
Can I ask what the estimators would be if you stop having just seen $k$ different buses? –  Anush Dec 7 '12 at 20:01
    
@Anush: It depends on why you stop. Do you stop at a predetermined $k$? Or randomly, e.g. with a certain probability after each bus? –  joriki Dec 7 '12 at 20:18
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