Density estimation in line transect sampling with grouped data by local least squares | Zendy

Barabesi Lucio | Zendy; Greco Luigi | Zendy; Naddeo Stefania | Zendy

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Density estimation in line transect sampling with grouped data by local least squares

Author(s) -

Barabesi Lucio,

Greco Luigi,

Naddeo Stefania

Publication year - 2002

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/env.524

Subject(s) - estimator , statistics , mathematics , monte carlo method , kernel density estimation , sampling (signal processing) , kernel (algebra) , least squares function approximation , econometrics , computer science , filter (signal processing) , combinatorics , computer vision

A semiparametric estimator for animal density in distance sampling is proposed when grouped data are on hand. The estimation technique is based on a kernel‐smoothed local least‐square criterion function, suitably developed for this setting. The new method presents interesting theoretical properties and it produces accurate and robust estimators, as is shown in a Monte Carlo study. Copyright © 2002 John Wiley & Sons, Ltd.

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