Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Savitsky, Terrance D."'
The mean field variational Bayes (VB) algorithm implemented in Stan is relatively fast and efficient, making it feasible to produce model-estimated official statistics on a rapid timeline. Yet, while consistent point estimates of parameters are achie
Externí odkaz:
http://arxiv.org/abs/2407.04659
The recent proliferation of computers and the internet have opened new opportunities for collecting and processing data. However, such data are often obtained without a well-planned probability survey design. Such non-probability based samples cannot
Externí odkaz:
http://arxiv.org/abs/2312.05383
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
We propose a novel Bayesian framework for the joint modeling of survey point and variance estimates for count data. The approach incorporates an induced prior distribution on the modeled true variance that sets it equal to the generating variance of
Externí odkaz:
http://arxiv.org/abs/2210.14366
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
Survey data are often collected under multistage sampling designs where units are binned to clusters that are sampled in a first stage. The unit-indexed population variables of interest are typically dependent within cluster. We propose a Fully Bayes
Externí odkaz:
http://arxiv.org/abs/2101.06237
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
Autor:
Savitsky, Terrance D.1 (AUTHOR), León-Novelo, Luis G.2 (AUTHOR) Luis.G.LeonNovelo@uth.tmc.edu, Engle, Helen3 (AUTHOR)
Publikováno v:
Journal of Official Statistics (JOS). Mar2024, Vol. 40 Issue 1, p161-189. 29p.
