Adaptive cluster sampling with a data driven stopping rule

The adaptive cluster sampling (ACS) is a suitable sampling design for rare and clustered populations. In environmental and ecological applications, biological populations are generally animals or plants with highly patchy spatial distribution. However, ACS would be a less efficient design when the s...

Full description

Saved in:
Bibliographic Details
Published in:Statistical methods & applications 2011-03, Vol.20 (1), p.1-21
Main Authors: Gattone, Stefano A., Di Battista, Tonio
Format: Article
Language:English
Subjects:
Adaptive cluster sampling
Chemistry and Earth Sciences
Cluster analysis
Computer Science
Economics
Efficiency
Finance
Generalized linear models
Health Sciences
Humanities
Insurance
Law
Management
Mathematics and Statistics
Medicine
Monte Carlo simulation
Neighborhoods
Physics
Random variables
Sample size
Statistical methods
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
Stopping rule
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The adaptive cluster sampling (ACS) is a suitable sampling design for rare and clustered populations. In environmental and ecological applications, biological populations are generally animals or plants with highly patchy spatial distribution. However, ACS would be a less efficient design when the study population is not rare with low aggregation since the final sample size could be easily out of control. In this paper, a new variant of ACS is proposed in order to improve the performance (in term of precision and cost) of ACS versus simple random sampling (SRS). The idea is to detect the optimal sample size by means of a data-driven stopping rule in order to determine when to stop the adaptive procedure. By introducing a stopping rule the theoretical basis of ACS are not respected and the behaviour of the ordinary estimators used in ACS is explored by using Monte Carlo simulations. Results show that the proposed variant of ACS allows to control the effective sample size and to prevent from excessive efficiency loss typical of ACS when the population is less clustered than anticipated. The proposed strategy may be recommended especially when no prior information about the population structure is available as it does not require a prior knowledge of the degree of rarity and clustering of the population of interest.
ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-010-0149-5