Design variations in adaptive web sampling

Date created: 
2008
Keywords: 
Hidden populations
Link-tracing designs
Markov chain Monte Carlo
Sampling
Adaptive sampling
Link-tracing designs
Markov chain Monte Carlo
Network sampling
Rao-Blackwellization
Spatial sampling
Abstract: 

There is an increasing body of literature related to sampling for network and spatial settings. Although current link-tracing methods like adaptive cluster sampling, snowball sampling, and targeted random walk designs have advantages over conventional designs, some of the following drawbacks remain evident: there is a lack of flexibility in sample placement; thereis an inability to control over sample sizes; and efficiency gains over conventional sampling designs for estimating population parameters may not be achievable. Adaptive web sampling (AWS) is a recently developed link-tracing design that overcomes some of these issues. Furthermore, the flexibility inherent to the AWS method permits many design variations. Using a simulated network population, an empirical population at risk for HIV/AIDS, a simulated spatial population, and an empirical population of birds, this project performs a simulation study to compare the performance of three variations of AWS strategies.

Description: 
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Language: 
English
Document type: 
Thesis
Rights: 
Copyright remains with the author
File(s): 
Supervisor(s): 
S
Department: 
Dept. of Statistics and Actuarial Science - Simon Fraser University
Thesis type: 
Project (M.Sc.)
Statistics: