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, 1983 Bayesian inference for nonprobability samples with nonignorable missingness
Statistical Analysis and Data Mining: The ASA Data Science JournalAbstractNonprobability 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.
Zhan Liu +3 more
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Domain estimation from weighted nonprobability samples
Statistical Journal of the IAOSWhen inferring population characteristics from a nonprobability sample, it is crucial to correct the possible selection bias therein by, for example, pseudo-weighting. Many correction methods focus on estimating the population means of the target variable. However, often the quantities of subpopulations are also of interest.
An-Chiao Liu +3 more
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