Bayes-Raking: Bayesian Finite Population Inference with Known Margins. (17th June 2020)

• Record Type: Journal Article
• Authors: Si, Yajuan; Zhou, Peigen
• Is Part Of: Journal of Survey Statistics and Methodology. Volume 9:Number 4(2021)
• Page Start: 833
• Page End: 855
• Publication Date: 2020-06-17
• Subjects: Bayes-raking -- Multilevel regression and poststratification -- Raking -- Soft constraints -- Solution subspace; Surveys -- Methodology -- Periodicals; Surveys -- Evaluation -- Periodicals; Sampling (Statistics) -- Periodicals; 001.433
• DOI: 10.1093/jssam/smaa008
• Languages: English
• ISSNs: 2325-0984

Abstract

Abstract: Raking is widely used for categorical data modeling and calibration in survey practice but faced with methodological and computational challenges. We develop a Bayesian paradigm for raking by incorporating the marginal constraints as a prior distribution via two main strategies: (1) constructing solution subspaces via basis functions or the projection matrix and (2) modeling soft constraints. The proposed Bayes-raking estimation integrates the models for the margins, the sample selection and response mechanism, and the outcome as a systematic framework to propagate all sources of uncertainty. Computation is done via Stan, and codes are ready for public use. Simulation studies show that Bayes-raking can perform as well as raking with large samples and outperform in terms of validity and efficiency gains, especially with a sparse contingency table or dependent raking factors. We apply the new method to the longitudinal study of well-being study and demonstrate that model-based approaches significantly improve inferential reliability and substantive findings as a unified survey inference framework.