Tuning parameter selection for a penalized maximum likelihood estimator of species richness

Abstract

Our goal is estimating the true number of classes in a population. We focus on the scenario where multiple frequency count tables have been collected from the same population. In this setting we demonstrate the efficacy of a previously published penalized maximum likelihood method. Four novel methods to tune the requisite penalization parameter are proposed. The performance of all proposed tuning methods is compared in simulations.