Tuning parameter selection for a penalized estimator of species richness

DOI10.1080/02664763.2020.1754359OpenAlexW3019283897MaRDI QIDQ5861553

Alex Paynter, Amy D. Willis

Publication date: 1 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2020.1754359

zbMATH Keywords

regularizationmaximum likelihooddiversityecologymicrobiome

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• R
• foreach
• Rcpp
• magrittr
• tidyverse
• data.table
• DESeq2
• breakaway

Cites Work

• Unnamed Item
• Estimating diversity via frequency ratios
• Objective Bayesian estimation for the number of species
• Population size estimation based upon ratios of recapture probabilities
• Estimating the Number of Species in a Stochastic Abundance Model
• The poisson-inverse gaussian disiribuiion as a model for species abundance
• A Unified Treatment of Integer Parameter Models
• Estimating the Size of a Truncated Sample
• On Fitting the Poisson Lognormal Distribution to Species-Abundance Data
• A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation

This page was built for publication: Tuning parameter selection for a penalized estimator of species richness