Estimating the number of unseen species: A bird in the hand is worth $\log n $ in the bush
This provides a foundational improvement for species estimation in ecology, genetics, and other fields, enabling predictions for much larger sample sizes with provable guarantees.
The paper tackles the problem of estimating the number of unseen species from samples, showing that their estimators can provably predict this number for populations up to log n times larger than observed, with optimal mean-square error and applicability across standard sampling models.
Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by Fisher, uses $n$ samples to predict the number $U$ of hitherto unseen species that would be observed if $t\cdot n$ new samples were collected. Of considerable interest is the largest ratio $t$ between the number of new and existing samples for which $U$ can be accurately predicted. In seminal works, Good and Toulmin constructed an intriguing estimator that predicts $U$ for all $t\le 1$, thereby showing that the number of species can be estimated for a population twice as large as that observed. Subsequently Efron and Thisted obtained a modified estimator that empirically predicts $U$ even for some $t>1$, but without provable guarantees. We derive a class of estimators that $\textit{provably}$ predict $U$ not just for constant $t>1$, but all the way up to $t$ proportional to $\log n$. This shows that the number of species can be estimated for a population $\log n$ times larger than that observed, a factor that grows arbitrarily large as $n$ increases. We also show that this range is the best possible and that the estimators' mean-square error is optimal up to constants for any $t$. Our approach yields the first provable guarantee for the Efron-Thisted estimator and, in addition, a variant which achieves stronger theoretical and experimental performance than existing methodologies on a variety of synthetic and real datasets. The estimators we derive are simple linear estimators that are computable in time proportional to $n$. The performance guarantees hold uniformly for all distributions, and apply to all four standard sampling models commonly used across various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product.