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Adaptive Cluster Sampling (adaptive + cluster_sampling)

Distribution by Scientific Domains
Mathematics and Statistics80%

Selected Abstracts

Estimating population size and habitat associations of two federally endangered mussels in the St. Croix River, Minnesota and Wisconsin, USA,

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS, Issue 3 2010
Daniel J. Hornbach
Abstract 1.North America is a globally important centre of freshwater mussel biodiversity. Accurate population estimates and descriptions of critical habitat for endangered species of mussels are needed but are hindered by their patchy distribution and the dynamic nature of their habitat. Adaptive cluster sampling (ACS) was used to estimate population size and habitat associations of two federally endangered species, Higgins eye (Lampsilis higginsii) and winged mapleleaf (Quadrula fragosa), in the St. Croix River. 2.This river holds the largest known winged mapleleaf population in the upper Mississippi River and contains Essential Habitat Areas for Higgins eye. Winged mapleleaf density ranged from 0.008,0.020 individuals m,2 (coefficient of variation=50,66%), yielding an estimate of 13 000 winged mapleleaf in this reach of the river. Higgins eye density varied from 0.008,0.015 individuals m,2 (coefficient of variation=66,167%) giving an estimate of 14 400 individuals in this area. 3.Higgins eye and winged mapleleaf were associated with areas of the overall highest mussel density and species richness, suggesting these endangered species occur in ,premier' mussel habitat. There were no differences in many microhabitat factors for sites with and without either endangered species. Select hydraulic measures (such as shear velocity and shear stress) showed significant differences in areas with and without the winged mapleleaf but not for Higgins eye. Areas that are less depositional support dense and diverse mussel assemblages that include both endangered species, with winged mapleleaf having a narrower habitat range than Higgins eye. 4.This study suggests that ACS can provide statistically robust estimates of density with 2,3 times more efficiency than simple random sampling. ACS, however, was quite time consuming. This work confirmed that of others demonstrating that larger-scale hydraulic parameters might be better predictors of prime mussel habitat than fine-scaled microhabitat factors. Using hydraulic measures may allow improved identification of potentially critical mussel habitat. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Ratio estimators in adaptive cluster sampling

ENVIRONMETRICS, Issue 6 2007
Arthur L. Dryver
Abstract In most surveys data are collected on many items rather than just the one variable of primary interest. Making the most use of the information collected is a issue of both practical and theoretical interest. Ratio estimates for the population mean or total are often more efficient. Unfortunately, ratio estimation is straightforward with simple random sampling, but this is often not the case when more complicated sampling designs are used, such as adaptive cluster sampling. A serious concern with ratio estimates introduced with many complicated designs is lack of independence, a necessary assumption. In this article, we propose two new ratio estimators under adaptive cluster sampling, one of which is unbiased for adaptive cluster sampling designs. The efficiencies of the new estimators to existing unbiased estimators, which do not utilize the auxiliary information, for adaptive cluster sampling and the conventional ratio estimation under simple random sampling without replacement are compared in this article. Related result shows the proposed estimators can be considered as a robust alternative of the conventional ratio estimator, especially when the correlation between the variable of interest and the auxiliary variable is not high enough for the conventional ratio estimator to have satisfactory performance. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Improved unbiased estimators in adaptive cluster sampling

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2005
Arthur L. Dryver
Summary., The usual design-unbiased estimators in adaptive cluster sampling are easy to compute but are not functions of the minimal sufficient statistic and hence can be improved. Improved unbiased estimators obtained by conditioning on sufficient statistics,not necessarily minimal,are described. First, estimators that are as easy to compute as the usual design-unbiased estimators are given. Estimators obtained by conditioning on the minimal sufficient statistic which are more difficult to compute are also discussed. Estimators are compared in examples. [source]