Research Information System
Gazi University Journal of Science, vol.33, no.1, pp.229-236, 2020 (ESCI, Scopus, TRDizin)
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 (Ĥ̂o) was compared to bias-corrected MLE (MLEBC), Jackknife (JK) and the proposed estimator of Chao and Shen (Ĥ̂CS ) using Principle component analysis (PCA). According to the result of the first PCA applied to the data including the estimators’ values of the assemblages, Ĥ̂o is located between JK and Ĥ̂CS 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 Ĥ̂o is still located between JK and Ĥ̂CS but its’ nearest neighbor becomes Ĥ̂CS along the first axis at this time. Those results indicated that Ĥ̂o is a better estimator than MLEBC. Thus the new proposed estimator (Ĥ̂o) may also be used as an alternative bias-corrected estimator in addition to the other estimators.