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Bayesian Methods to Improve The Accuracy of Differentially Private Measurements of Constrained Parameters
Ryan Janicki, Scott H. Holan, Kyle M. Irimata +2
Formal disclosure avoidance techniques are necessary to ensure that published data can not be used to identify information about individuals. The addition of statistical noise to u…
Computationally Efficient Bayesian Unit-Level Models for Non-Gaussian Data Under Informative Sampling
Paul A. Parker, Scott H. Holan, Ryan Janicki
Statistical estimates from survey samples have traditionally been obtained via design-based estimators. In many cases, these estimators tend to work well for quantities such as pop…
Conjugate Bayesian Unit-level Modeling of Count Data Under Informative Sampling Designs
Paul A. Parker, Scott H. Holan, Ryan Janicki
Unit-level models for survey data offer many advantages over their area-level counterparts, such as potential for more precise estimates and a natural benchmarking property. Howeve…
Unit Level Modeling of Survey Data for Small Area Estimation Under Informative Sampling: A Comprehensive Overview with Extensions
Paul A. Parker, Ryan Janicki, Scott H. Holan
Model-based small area estimation is frequently used in conjunction with survey data in order to establish estimates for under-sampled or unsampled geographies. These models can be…