Bayesian estimation for finite population proportion under selection bias via surrogate samples
Seong Mi Choi, Dal Ho Kim · Journal of the Korean Data and Information Science Society · 2013
In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.