NDLI: Randomized graph sampling

Content Provider	Springer Nature Link
Author	Ducey, Mark J.
Copyright Year	2011
Abstract	Randomized graph sampling (RGS) is an approach for sampling populations associated with or describable as graphs, when the structure of the graph is known and the parameter of interest is the total weight of the graph. RGS is related to, but distinct from, other graph-based approaches such as snowball and network sampling. Graph elements are clustered into walks that reflect the structure of the graph, as well as operational constraints on sampling. The basic estimator in RGS can be constructed as a Horvitz-Thompson estimator. I prove it to be design-unbiased, and also show design-unbiasedness of an estimator of the sample variance when walks are sampled with replacement. Covariates can be employed for variance reduction either through improved assignment of selection probabilities to walks in the design step, or through the use of alternative estimators during analysis. The approach is illustrated with a trail maintenance example, which demonstrates that complicated approaches to assignment of selection probabilities can be counterproductive. I describe conditions under which RGS may be efficient in practice, and suggest possible applications.
Starting Page	1
Ending Page	21
Page Count	21
File Format	PDF
ISSN	13528505
Journal	Environmental and Ecological Statistics
Volume Number	19
Issue Number	1
e-ISSN	15733009
Language	English
Publisher	Springer US
Publisher Date	2011-03-23
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Network sampling Cluster sampling Generalized ratio estimator Evolutionary Biology Mathematical and Computational Biology Statistics Ecology
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Environmental Science Statistics, Probability and Uncertainty

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