A New Proposed Estimator for Reducing Bias Due to Undetected Species

Ozkan K.

GAZI UNIVERSITY JOURNAL OF SCIENCE, vol.33, no.1, pp.229-236, 2020 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.35378/gujs.554644
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Metadex, Civil Engineering Abstracts, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.229-236
  • Süleyman Demirel University Affiliated: Yes


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 ((H) over cap (o)) was compared to bias-corrected MLE (MLEBc), Jackknife (JK) and the proposed estimator of Chao and Shen ((H) over cap (CS)) using Principle component analysis (PCA). According to the result of the first PCA applied to the data including the estimators' values of the assemblages, (H) over cap (o) is located between JK and (H) over cap (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 (H) over cap (o) is still located between JK and (H) over cap (CS) but its' nearest neighbor becomes (H) over cap (CS) along the first axis at this time. Those results indicated that (H) over cap (o) is a better estimator than MLEBc. Thus the new proposed estimator ((H) over cap (o)) may also be used as an alternative bias-corrected estimator in addition to the other estimators.