Zobrazeno 1 - 10
of 147
pro vyhledávání: '"Si, Yajuan"'
In general, it is challenging to release differentially private versions of survey-weighted statistics with low error for acceptable privacy loss. This is because weighted statistics from complex sample survey data can be more sensitive to individual
Externí odkaz:
http://arxiv.org/abs/2411.04236
In the absence of comprehensive or random testing throughout the COVID-19 pandemic, we have developed a proxy method for synthetic random sampling to estimate the actual viral incidence in the community, based on viral RNA testing of asymptomatic pat
Externí odkaz:
http://arxiv.org/abs/2405.05909
When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres
Externí odkaz:
http://arxiv.org/abs/2309.09115
Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations could be heterogeneous across individuals in the population, as indicate
Externí odkaz:
http://arxiv.org/abs/2307.00129
Autor:
Li, Katherine, Si, Yajuan
Health disparity research often evaluates health outcomes across demographic subgroups. Multilevel regression and poststratification (MRP) is a popular approach for small subgroup estimation due to its ability to stabilize estimates by fitting multil
Externí odkaz:
http://arxiv.org/abs/2205.02775
Longitudinal studies are subject to nonresponse when individuals fail to provide data for entire waves or particular questions of the survey. We compare approaches to nonresponse bias analysis (NRBA) in longitudinal studies and illustrate them on the
Externí odkaz:
http://arxiv.org/abs/2204.07105
Autor:
Si, Yajuan, Bandoli, Gretchen, Cole, Katherine M., Daniele Fallin, M., Stuart, Elizabeth A., Gurka, Kelly K., Althoff, Keri N., Thompson, Wesley K.
Publikováno v:
In Developmental Cognitive Neuroscience October 2024 69
Autor:
Si, Yajuan
The thesis develops nonparametric Bayesian models to handle incomplete categorical variables in data sets with high dimension using the framework of multiple imputation. It presents methods for ignorable missing data in cross-sectional studies, and p
Externí odkaz:
http://hdl.handle.net/10161/5837
Explicit knowledge of total community-level immune seroprevalence is critical to developing policies to mitigate the social and clinical impact of SARS-CoV-2. Publicly available vaccination data are frequently cited as a proxy for population immunity
Externí odkaz:
http://arxiv.org/abs/2202.09247
Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden
Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validi
Externí odkaz:
http://arxiv.org/abs/2104.04435