Content area
Abstract
The maximum likelihood estimator (MLE) of Gini-Simpson’s diversity index (GS) is widely used but suffers from large bias when the number of species is large or infinite. We propose a new estimator of the GS index and show its unbiasedness. Asymptotic normality of the proposed estimator is established when the number of species in the population is finite and known, finite but unknown, and infinite. Simulations demonstrate advantages of our estimator over the MLE, and a real example for the extinction of dinosaurs endorses the use of our approach. Mathematics Subject Classification (MSC) codes is 60E05, which refers to distributions: general theory.
Details
Title
A new diversity estimator
Author
Zheng, Lukun 1 ; Jiang, Jiancheng 2
1 Department of Mathematics, Tennessee Technological University, Cookeville, TN, USA
2 Department of Mathematics and Statistics, UNC Charlotte, 28223Charlotte, USA
1 Department of Mathematics, Tennessee Technological University, Cookeville, TN, USA
2 Department of Mathematics and Statistics, UNC Charlotte, 28223Charlotte, USA
Pages
1-13
Section
International Conference on Statistical Distributions and Applications, ICOSDA 2016
Publication year
2017
Publication date
Sep 2017
e-ISSN
21955832
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1956856831
Copyright
Journal of Statistical Distributions and Applications is a copyright of Springer, 2017.