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Local likelihood density estimation in line transect sampling
Author(s) -
Barabesi Lucio
Publication year - 2000
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/1099-095x(200007/08)11:4<413::aid-env422>3.0.co;2-p
Subject(s) - estimator , nonparametric statistics , statistics , mathematics , parametric statistics , monte carlo method , density estimation , probability density function , likelihood function , sampling (signal processing) , semiparametric model , maximum likelihood , computer science , filter (signal processing) , computer vision
A novel semiparametric estimator for the probability density function of detected distances in line transect sampling is proposed. The estimator is obtained using a local likelihood density estimation approach, a technique recently proposed which affords the advantages of both parametric and nonparametric methods, i.e. accuracy and robustness. Moreover, a procedure for the selection of the local likelihood bandwidth is obtained. The performance of the proposed estimator with respect to some existing nonparametric and semiparametric estimators is assessed by means of a Monte Carlo study. Finally, a real data set is analyzed. Copyright © 2000 John Wiley & Sons, Ltd.