Approximate Bayesian Computation Estimator for Respondent-Driven Sampling
نویسنده
چکیده
Respondent-driven sampling is a network-based technique to collect information and make estimation about behavior and composition of social groups in hidden population. The non-randomly selected samples prohibit the use of the sample mean as a statistically valid estimator. Researchers have proposed several asymptotically unbiased estimators, but many fail to realize that the high variance of these estimators inevitably leads to attenuated performance. We propose to use a Bayesian estimator in the hope of achieving better precision by reducing variance of the estimator.
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