A New Proposed Estimator for Reducing Bias Due to Undetected Species

Year 2020, , 229 - 236, 01.03.2020

Kürşad Özkan

https://doi.org/10.35378/gujs.554644

Cited By: 3

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 (Ĥ₀) was compared to bias-corrected MLE (MLE_BC), Jackknife (JK) and the proposed estimator of Chao and Shen (Ĥ_cs)  using Principle component analysis (PCA). The
result of the first PCA applied to the data including the estimators’ values of
the assemblages showed that  Ĥ₀ 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 Ĥ₀ 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 Ĥ₀ is a better
estimator than MLE_BC. Thus the new proposed estimator (Ĥ₀) 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

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