Near-optimal estimation of the unseen under regularly varying tail populations

Given $n$ samples from a population of individuals belonging to different species, what is the number $U$ of hitherto unseen species that would be observed if $\lambda n$ new samples were collected? This is an important problem in many scientific endeavors, and it has been the subject of recent works introducing non-parametric estimators of $U$ that are minimax near-optimal and consistent all the way up to $\lambda\asymp\log n$. These works do not rely on any assumption on the underlying unknown distribution $p$ of the population, and therefore, while providing a theory in its greatest generality, worst-case distributions may severely hamper the estimation of $U$ in concrete applications. In this paper, we consider the problem of strengthening the non-parametric framework for estimating $U$. Inspired by the estimation of rare probabilities in extreme value theory, and motivated by the ubiquitous power-law type distributions in many natural and social phenomena, we make use of a semi-parametric assumption regular variation of index $\alpha\in(0,1)$ for the tail behaviour of $p$. Under this assumption, we introduce an estimator of $U$ that is simple, linear in the sampling information, computationally efficient, and scalable to massive datasets. Then, uniformly over our class of regularly varying tail distributions, we show that the proposed estimator has provable guarantees: i) it is minimax near-optimal, up to a power of $\log n$ factor; ii) it is consistent all of the way up to $\log \lambda\asymp n^{\alpha/2}/\sqrt{\log n}$, and this range is the best possible. This work presents the first study on the estimation of the unseen under regularly varying tail distributions. A numerical illustration of our methodology is presented for synthetic data and real data.