= Variance of the Watterson estimator
{title2=$\operatorname{Var}(\widehat\theta_W)=\frac{\theta}{H_{n-1}}+\frac{\theta^2H_{n-1}^{(2)}}{H_{n-1}^2}$}
The <moments of the mutation count on a neutral coalescent tree> give this <variance> for $\widehat\theta_W=S/H_{n-1}$. Since $H_{n-1}\sim\log n$ and the sum of inverse squares is bounded, it tends to zero. Together with unbiasedness, this gives <mean-square convergence> to the mutation parameter, so it is a <consistent estimator>.
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