A New Proposed Estimator for Reducing Bias Due to Undetected Species

Authors : Kürşad ÖZKAN

Pages : 229-236

Doi:10.35378/gujs.554644

View : 18 | Download : 10

Publication Date : 2020-03-01

Article Type : Research Paper

Abstract :The present paper addresses a new approach to reduce bias when there are undetected species in a plot. Partially density matrix plays essential role in this new proposed estimator. The performance of the new proposed estimator  insert ignore into journalissuearticles values( Ĥ 0 );  was compared to bias-corrected MLE insert ignore into journalissuearticles values(MLE BC );, Jackknife insert ignore into journalissuearticles values(JK); and the proposed estimator of Chao and Shen  insert ignore into journalissuearticles values(Ĥ cs );    using Principle component analysis insert ignore into journalissuearticles values(PCA);. The result of the first PCA applied to the data including the estimators’ values of the assemblages showed that   Ĥ 0   is located between JK and  Ĥ cs   and its’ nearest neighbor becomes JK. The second PCA was applied to the data belonging to the relative estimator values between the pairwise assemblages and, it was found that  Ĥ 0  is still located between JK and  Ĥ cs  but its’ nearest neighbor becomes  Ĥ cs  in this time along the first axis. Those results were evaluated that  Ĥ 0  is a better estimator than MLE BC . Thus the new proposed estimator insert ignore into journalissuearticles values( Ĥ 0 );  can also be used as an alternative bias-corrected estimator in addition to the other estimators.
Keywords : Bias corrected estimator, Diversity, Entropy, Jackknife, Partially density matrix