Bayesian nonparametric estimation of the probability of discovering new species

• 成果类型: Article
• 署名作者: Lijoi, Antonio; Mena, Ramses H.; Prunster, Igor
• 署名单位: University of Pavia; Universidad Nacional Autonoma de Mexico; University of Turin
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asm061
• 发表日期: 2007
• 页码: 769786
• 关键词: random partitions; number; MODEL; population; coverage; distributions; prediction; sample

摘要

We consider the problem of evaluating the probability of discovering a certain number of new species in a new sample of population units, conditional on the number of species recorded in a basic sample. We use a Bayesian nonparametric approach. The different species proportions are assumed to be random and the observations from the population exchangeable. We provide a Bayesian estimator, under quadratic loss, for the probability of discovering new species which can be compared with well-known frequentist estimators. The results we obtain are illustrated through a numerical example and an application to a genomic dataset concerning the discovery of new genes by sequencing additional single-read sequences of cDNA fragments.