eCite Digital Repository

Clustering and abundance estimation for Neyman-Scott models and line transect surveys


Brown, BM and Cowling, A, Clustering and abundance estimation for Neyman-Scott models and line transect surveys, Biometrika, 85, (2) pp. 427-438. ISSN 0006-3444 (1998) [Refereed Article]

DOI: doi:10.1093/biomet/85.2.427


This paper considers the estimation of clustering parameters and mean species intensity based on likelihood theory for the simplified Neyman-Scott Poisson model, with observations taken from line transect surveys with a Gaussian detection function. The estimators and accompanying standard error expressions are tractable and easy to calculate, and, coming from likelihood methods, often will have high efficiency. Such properties compare favourably with those of existing K-function methods which however are semiparametric in nature and less reliant on specific parametric assumptions. The likelihood analysis reveals auxiliary information which could be used to check the form of the detection function. Clustering; Cramer-Rao bound; Detection function; K-function; Line transect survey; Maximum likelihood; Neyman-Scott line transect distribution; Neyman-Scott Poisson process.

Item Details

Item Type:Refereed Article
Research Division:Mathematical Sciences
Research Group:Pure mathematics
Research Field:Algebra and number theory
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Brown, BM (Dr Bruce Brown)
ID Code:14310
Year Published:1998
Web of Science® Times Cited:4
Deposited By:Mathematics
Deposited On:1998-08-01
Last Modified:2011-08-09

Repository Staff Only: item control page