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- W2938226775 abstract "When random effects are correlated with sample design variables, the usualapproach of employing individual survey weights (constructed to be inverselyproportional to the unit survey inclusion probabilities) to form apseudo-likelihood no longer produces asymptotically unbiased inference. Weconstruct a weight-exponentiated formulation for the random effectsdistribution that achieves unbiased inference for generating hyperparameters ofthe random effects. We contrast our approach with frequentist methods that relyon numerical integration to reveal that only the Bayesian method achieves bothunbiased estimation with respect to the sampling design distribution andconsistency with respect to the population generating distribution. Oursimulations and real data example for a survey of business establishmentsdemonstrate the utility of our approach across different modelling formulationsand sampling designs. This work serves as a capstone for recent developmentalefforts that combine traditional survey estimation approaches with the Bayesianmodeling paradigm and provides a bridge across the two rich but disparatesub-fields." @default.
- W2938226775 created "2019-04-25" @default.
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- W2938226775 date "2019-04-16" @default.
- W2938226775 modified "2023-09-27" @default.
- W2938226775 title "Bayesian Mixed Effects Model Estimation under Informative Sampling" @default.
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- W2938226775 hasPublicationYear "2019" @default.
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