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pro vyhledávání: '"Williams, Matthew R."'
Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. \sw{Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection
Externí odkaz:
http://arxiv.org/abs/2310.01575
We present csSampling, an R package for estimation of Bayesian models for data collected from complex survey samples. csSampling combines functionality from the probabilistic programming language Stan (via the rstan and brms R packages) and the handl
Externí odkaz:
http://arxiv.org/abs/2308.06845
Autor:
Savitsky, Terrance D., Williams, Matthew R., Gershunskaya, Julie, Beresovsky, Vladislav, Johnson, Nels G.
Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomized survey (reference) sample by increasing the effective sample size. E
Externí odkaz:
http://arxiv.org/abs/2208.14541
This paper introduces a new method that embeds any Bayesian model used to generate synthetic data and converts it into a differentially private (DP) mechanism. We propose an alteration of the model synthesizer to utilize a censored likelihood that in
Externí odkaz:
http://arxiv.org/abs/2205.05003
We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record likelihood contributions with weights $\in [0,1]$ based on their ident
Externí odkaz:
http://arxiv.org/abs/2101.06188
We address practical implementation of a risk-weighted pseudo posterior synthesizer for microdata dissemination with a new re-weighting strategy that maximizes utility of released synthetic data under at any level of formal privacy guarantee. Our re-
Externí odkaz:
http://arxiv.org/abs/2006.01230
We devise survey-weighted pseudo posterior distribution estimators under two-stage informative sampling of both primary clusters and secondary nested units for a one-way analysis of variance (ANOVA) population generating model as a simple canonical c
Externí odkaz:
http://arxiv.org/abs/2004.06191
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We propose a Bayesian pseudo posterior mechanism to generate record-level synthetic databases equipped with an $(\epsilon,\delta)-$ probabilistic differential privacy (pDP) guarantee, where $\delta$ denotes the probability that any observed database
Externí odkaz:
http://arxiv.org/abs/1909.11796