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Robust Density Estimation Through Distance Measurements
Author(s) -
Delince J.
Publication year - 1986
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.2307/1939088
Subject(s) - estimator , poisson distribution , statistics , mathematics , estimation , density estimation , econometrics , point (geometry) , population , frame (networking) , k nearest neighbors algorithm , computer science , ecology , artificial intelligence , biology , engineering , telecommunications , geometry , demography , systems engineering , sociology
A minor modification to Morisita's method permits the definition of a robust density estimator based on nearest neighbor by sectors. In a finite population frame, these distances are used to evaluate the probability of selecting the nearest individual to a point chosen at random. In simulations of regular, Poisson, and aggregated patterns, the estimator was shown to be robust not only with regard to unbiasedness, but also to efficiency. This is of particular interest in aggregated cases.