A New Estimator of the Discovery Probability
Article first published online: 1 OCT 2012
© 2012, The International Biometric Society
Volume 68, Issue 4, pages 1188–1196, December 2012
How to Cite
Favaro, S., Lijoi, A. and Prünster, I. (2012), A New Estimator of the Discovery Probability. Biometrics, 68: 1188–1196. doi: 10.1111/j.1541-0420.2012.01793.x
- Issue published online: 21 DEC 2012
- Article first published online: 1 OCT 2012
- Received November 2011. Revised April 2012. Accepted May 2012.
Vol. 69, Issue 3, 797, Article first published online: 23 SEP 2013
- Bayesian nonparametrics;
- Gibbs-type priors;
- Rare species discovery;
- Species sampling models;
- Two-parameter Poisson–Dirichlet process
Summary Species sampling problems have a long history in ecological and biological studies and a number of issues, including the evaluation of species richness, the design of sampling experiments, and the estimation of rare species variety, are to be addressed. Such inferential problems have recently emerged also in genomic applications, however, exhibiting some peculiar features that make them more challenging: specifically, one has to deal with very large populations (genomic libraries) containing a huge number of distinct species (genes) and only a small portion of the library has been sampled (sequenced). These aspects motivate the Bayesian nonparametric approach we undertake, since it allows to achieve the degree of flexibility typically needed in this framework. Based on an observed sample of size n, focus will be on prediction of a key aspect of the outcome from an additional sample of size m, namely, the so-called discovery probability. In particular, conditionally on an observed basic sample of size n, we derive a novel estimator of the probability of detecting, at the (n+m+1)th observation, species that have been observed with any given frequency in the enlarged sample of size n+m. Such an estimator admits a closed-form expression that can be exactly evaluated. The result we obtain allows us to quantify both the rate at which rare species are detected and the achieved sample coverage of abundant species, as m increases. Natural applications are represented by the estimation of the probability of discovering rare genes within genomic libraries and the results are illustrated by means of two expressed sequence tags datasets.