Estimating the Number of Unseen Species: How Many Words did Shakespeare Know?

By Peter McCullagh. 2008

299 citations recorded by the source corpus.

Subfields: Cultural Studies, Ecological Modeling, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0d44c094f4924b85cf5e9124ca5bc1abad8a29ab

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Citations in the corpus

Bayesian Nonparametric Estimation of the Probability of Discovering New Species (2007, methodology): “A Bayesian approach is undertaken in Hill (1979), Boender & Rinnoy Kan (1987), Gandolfi & Sastri (2004) and Zhang & Stern (2005), whereas an empirical Bayes model is exploited in Efron & Thisted (1976).”
A large-scale study of web password habits (2007, background): “Efron and Fisher [4] use a Poisson distribution of the frequencies and derive an estimate”
Bayesian non‐parametric inference for species variety with a two‐parameter Poisson–Dirichlet process prior (2009, methodology): “5 times the basic sample, which allows to compare the results also with estimators, which become unstable for larger sizes of the additional sample such as the popular estimator of Efron and Thisted (1976). The sub–samples are obtained by sampling 1034 genes without replacement from the 2586 observe”
Handbook of Capture-Recapture Analysis (2010, background): “…(e.g., cases for a specific diseases, homeless persons, and mentally ill people [IWDGMF 1995]), the size of vocabulary for an author in linguistics (Efron and Thisted 1976), the number of undetected bugs in a piece of software in software reliability (Briand et al. 2000), and the number of…”
Instance optimal learning of discrete distributions (2016, background): “…lever-aged in [32, 34] with the goal of creating worst-case optimal estimators for entropy, and other distributional properties of interest, and a related heuristic was proposed in the 1970’s by Efron and Thisted [17], also with the goal of estimating properties of the underlying distribution.”
Maximum Likelihood Estimation of Functionals of Discrete Distributions (2014, background): “When α→ 0, the number of samples required for the MLE grows super-polynomially in S, which is consistent with the intuition that Fα(P ), α → 0 is essentially equivalent to the alphabet size of a distribution, whose estimation is known to be very hard when there may exist symbols with very small prob”
VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires (2015, methodology): “Using Chao1 estimate [47] for normalized datasets that has shown the best performance together with Efron estimate (yet is far simpler to compute) in the aforementioned benchmark, one can discover that MS samples have a significantly lower diversity than the controls (Fig 2B).”
Concentration inequalities in the infinite urn scheme for occupancy counts and the missing mass, with applications (2014, methodology): “This was posed primarily within the Poisson model in the original paper Fisher et al. [1943] and later by [Efron and Thisted, 1976], but the same question may also be asked in the binomial model.”
Minimax Estimation of Functionals of Discrete Distributions (2014, unknown): “Indeed, the limiting case of α = 0 corresponds to estimating the support size of a discrete distribution, which has long been known impossible to do consistently without additional assumptions [30] [31].”
Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects (2016, unknown): “Estimating the frequency distribution of genetic variation is closely related to the classic statistics problem of estimating the number of unseen animal species from capture experiments 13,14 .”
The Ubiquitous Ewens Sampling Formula (2016, background): “These classes may represent the grouping of animals according to species, as in Fisher, Corbet and Williams’s [41] and Good and Toulmin’s [44] consideration of the number of unseen species in a finite sample, or the grouping of literary works according to author, as in Efron and Thisted’s [30, 83] t”