Bayesian inference for nonprobability samples with nonignorable missingness
Zhan Liu,
Xuesong Chen,
Ruohan Li
et al.
Abstract:Nonprobability samples, especially web survey data, have been available in many different fields. However, nonprobability samples suffer from selection bias, which will yield biased estimates. Moreover, missingness, especially nonignorable missingness, may also be encountered in nonprobability samples. Thus, it is a challenging task to make inference from nonprobability samples with nonignorable missingness. In this article, we propose a Bayesian approach to infer the population based on nonprobability samples… Show more
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