Design variations in adaptive web sampling
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; there is 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.
Keywords: Adaptive sampling, Link-tracing designs, Markov chain Monte Carlo, Network sampling, Rao-Blackwellization, Spatial sampling
This type of interdisciplinary work is a hallmark of our program in Applied Statistics at Simon Fraser University. For more information, please contact Kyle Vincent (firstname.lastname@example.org) or his supervisor Steve Thompson (email@example.com), Department of Statistics and Actuarial Science.