A penalized nonparametric maximum likelihood approach to species richness estimation

• 成果类型: Article
• 署名作者: Wang, JPZ; Lindsay, BG
• 署名单位: Northwestern University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000002005
• 发表日期: 2005
• 页码: 942-959
• 关键词: estimating population-size; capture-recapture models; expressed sequence tags; finite population; number; Heterogeneity; sample; FAILURE; mle

摘要

We propose a class of penalized nonparametric maximum likelihood estimators (NPMLEs) for the species richness problem. We use a penalty term on the likelihood because likelihood estimators that lack it have an extreme instability problem. The estimators are constructed using a conditional likelihood that is simpler than the full likelihood. We show that the full-likelihood NPMLE solution given by Norris and Pollock can be found (with great accuracy) by using an appropriate penalty term on the conditional likelihood, so it is an element of our class of estimators. A simple and fast algorithm for the penalized NPMLE is developed; it can be used to greatly speed up computation of the unconditional NPMLE. It can also be used to find profile mixture likelihoods. Based on our goal of attaining high stability while retaining sensitivity, we propose an adaptive quadratic penalty function. A systematic simulation study, using a wide range of scenarios, establishes the success of this method relative to its competitors. Finally, we discuss an application in the gene number estimation using expressed sequence tag (EST) data from genomics.