Integrating representative and non-representative survey data for efficient inference
arxiv(2024)
摘要
Non-representative surveys are commonly used and widely available but suffer
from selection bias that generally cannot be entirely eliminated using
weighting techniques. Instead, we propose a Bayesian method to synthesize
longitudinal representative unbiased surveys with non-representative biased
surveys by estimating the degree of selection bias over time. We show using a
simulation study that synthesizing biased and unbiased surveys together
out-performs using the unbiased surveys alone, even if the selection bias may
evolve in a complex manner over time. Using COVID-19 vaccination data, we are
able to synthesize two large sample biased surveys with an unbiased survey to
reduce uncertainty in now-casting and inference estimates while simultaneously
retaining the empirical credible interval coverage. Ultimately, we are able to
conceptually obtain the properties of a large sample unbiased survey if the
assumed unbiased survey, used to anchor the estimates, is unbiased for all
time-points.
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