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stat.ME2026
When Bayes goes bad: Weakly-regularized covariate adjustment leads to a biased estimate of prevalence
Swen Kuh, Lauren Kennedy, Qixuan Chen +1
When estimating population prevalence from a non-random sample, it is important to adjust for differences between sample and population. However, adjustment for multiple factors re…
stat.ME2022★ 1 cited
Using leave-one-out cross-validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale
Swen Kuh, Lauren Kennedy, Qixuan Chen +1
In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on detail…