Wiley, Jun 7, 1996 - Business & Economics - 265 pages
Offering a viable solution to the long-standing problem of estimating the abundance of rare, clustered populations, adaptive sampling designs are rapidly gaining prominence in the natural and social sciences as well as in other fields with inherently difficult sampling situations. In marked contrast to conventional sampling designs, in which the entire sample of units to be observed is fixed prior to the survey, adaptive sampling strategies allow for increased sampling intensity depending upon observations made during the survey. For example, in a survey to assess the abundance of a rare animal species, neighboring sites may be added to the sample whenever the species is encountered during the survey. In an epidemiological survey of a contagious or genetically linked disease, sampling intensity may be increased whenever prevalence of the disease is encountered.
Written by two acknowledged experts in this emerging field, this book offers researchers their first comprehensive introduction to adaptive sampling. An ideal reference for statisticians conducting research in survey designs and spatial statistics as well as researchers working in the environmental, ecological, public health, and biomedical sciences.
* Provides a comprehensive, fully integrated introduction to adaptive sampling theory and practice
* Describes recent research findings
* Introduces readers to a wide range of adaptive sampling strategies and techniques
* Includes numerous real-world examples from environmental pollution studies, surveys of rare animal and plant species, studies of contagious diseases, marketing surveys, mineral and fossil-fuel assessments, and more
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FixedPopulation Sampling Theory
Stochastic Population Sampling Theory
Adaptive Cluster Sampling
8 other sections not shown
adaptive allocation adaptive cluster sampling adaptive design adaptive sampling adaptive strategy Chapter conditional density conditional expectation conventional design conventional or adaptive conventional strategy data value denote depend design-unbiased distribution edge units example fcth network final sample function given the sample indicator function intersection probabilities labels likelihood function linear maximum likelihood mean-square error multivariate neighborhood number of units observed obtained optimal order statistics outcome p(so parameter partition plots population mean population model population total possible samples predictive sufficient primary units random variables Rao-Blackwell theorem reduced data relative efficiency replacement sample mean sample sizes sampling design satisfying the condition secondary units Section simple random sampling spatial Stochastic strata stratified sampling stratum h study region sufficient statistic Suppose survey systematic sample Table Theorem Thompson total number tows unbiased estimator unit with value units is selected variable of interest variance estimator variance-covariance matrix vector y-values