A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation

1 September 2005

journal article
Published by Taylor & Francis in Journal of the American Statistical Association

Vol. 100 (471) , 942-959
https://doi.org/10.1198/016214504000002005

Abstract

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 lac...

Keywords

MIXTURE MODEL
NPMLE COMPUTING
NUMBER OF CLASSES
PENALIZED NPMLE
SPECIES RICHNESS

This publication has 1 reference indexed in Scilit:

EST clustering error evaluation and correction
Bioinformatics, 2004