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A New Proposed Estimator for Reducing Bias Due to Undetected Species

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  ( Ĥ 0 )  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 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 ( Ĥ 0 )  can also be used as an alternative bias-corrected estimator in addition to the other estimators.